case study on road construction

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• Published: 25 March 2022

Evaluating the impact of highway construction projects on landscape ecological risks in high altitude plateaus

• Chao Li 1 , 2 ,
• Jingxiao Zhang 1 ,
• Simon P. Philbin 3 ,
• Xu Yang 4 ,
• Zhanfeng Dong 5 ,
• Jingke Hong 6 &
• Pablo Ballesteros-Pérez 7  

Scientific Reports volume  12 , Article number:  5170 ( 2022 ) Cite this article

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• Environmental sciences

In China and other countries, many highway projects are built in extensive and high-altitude flat areas called plateaus. However, research on how the materialisation of these projects produce a series of ecological risks in the landscape is very limited. In this research, a landscape ecological risk analysis model for high-altitude plateaus is proposed. This model is based on the pattern of land uses of the surrounding area. Our study includes buffer analysis, spatial analysis, and geostatistical analysis. We apply the model to the Qumei to Gangba highway, a highway section located in the southeast city of Shigatse at the Chinese Tibet autonomous region. Through global and local spatial autocorrelation analysis, the spatial clustering distribution of ecological risks is also explored. Overall, our study reveals the spatial heterogeneity of ecological risks and how to better mitigate them. According to a comparison of the risk changes in two stages (before and after the highway construction), the impact of highway construction on the ecological environment can be comprehensively quantified. This research will be of interest to construction practitioners seeking to minimize the impact of highway construction projects on the ecological environment. It will also inform future empirical studies in the area of environmental engineering with potential affection to the landscape in high-altitude plateaus.

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Introduction

Since the beginning of the twenty-first century, the Chinese government has implemented specific construction plans for highways in the Tibet plateau. This has resulted in a major increase in the size and capacity of the highway network in this region 1 . However, the complex and special geological environment of the Tibet region has not been adequately considered in the construction process creating many negative effects. The progressive destruction of vegetation during highway construction has caused significant landscape changes along the highway route resulting in a fragmented landscape. At the same time, waste discharges and exhaust gases during the construction process have eroded the soil and will surely create further soil erosion and water pollution problems 2 . Recent research has been mainly focused on evaluating the impact of highway construction on urban areas 3 , 4 , 5 , lakes and river basins 6 , 7 and nature reserves 8 , 9 . However, hardly any research has investigated the impact of highway construction in ecosystems at high altitudes in the Plateau. In the case of Tibet, the complex terrain, climatic conditions and the difficulty of data acquisition, has resulted in very limited research.

The Tibet plateau is a densely distributed area of nature reserves and an important ecological security barrier between China and the wider continent of Asia. It plays an important role in climate regulation, soil and water conservation, biodiversity protection and carbon accumulation. However, due to the fragility of the very cold environment and its sensitivity to external disturbances, the overall landscape pattern of such areas can be easily fragmented (i.e. acquire poor stability and resilience to external changes). Consequently, intensive highway construction will likely result on major negative impacts on the biodiversity and landscape patterns of this area.

At present, methods for ecological risk assessment include various modelling approaches from diverse fields like physics, chemistry and biology. Many of them involve simulation as well as other risk measurement methods, such as the expert judgment. Physics, chemistry and biological-based simulation involve developing a model in order to observe and test the envisaged impact of events on the ecological properties of the ecosystem, including biological life. The general risk measurement method is used to estimate the importance of the risk, which is broken down in probability and severity. When there are many system property uncertainties or just different opinions on the ecological value of some of the system components, the expert judgment method can also become an alternative method. Similarly, ecological risk assessment methods based on remote sensing technologies and geographic information systems are becoming increasingly common. These technologies are also used to process and analyze changes in the ecological environment 10 , 11 . In terms of ecological risk evaluation indicators, there tends to be two main aspects analyzed: heavy metal pollution in soils and landscape pattern changes. Landscape ecological risk assessment pays more attention to spatio-temporal heterogeneity. Understanding this heterogeneity can help decision-making for regional risk prevention and improve landscape management 12 , 13 . However, empirical studies that confirm the advantages and application of these models are extremely limited.

This research study builds on existing research and adopts the Qumei to Gangba highway section in the southeast city of Shigatse. This city is located in the Tibet autonomous region of China. The study considers the 10-km buffer zone along the highway in a high-altitude plateau area and develops a landscape ecological risk assessment model based on the landscape pattern. The spatial and temporal distribution of the landscape ecological risks along the highway are evaluated before and after the highway construction. This analysis reveals the characteristics and extensive impacts of highway construction on the landscape pattern and the landscape ecological risks of the area. It will also provide an improved understanding of the technical support required for the post-construction stage of ecological restoration in these high-altitude areas.

Literature review

The impact of infrastructure construction on landscape patterns.

Engineering construction activities deeply affect the regional landscape and the level of biological activity, including human life. On the microscopic scale, construction activities frequently result in heavy metal pollution, which change the structure of the surface soil and affects the migration of species 14 . On the landscape scale, construction activities often lead to long-term fragmentation.

Based on GIS and the buffer analysis method, Minxi et al. 15 used several landscape metrics to quantitatively study the impact of hydropower projects construction on landscape structure changes. Yang et al. 16 investigated the process of hydropower facility development and the ecological characteristics of a river basin. Then, they systematically evaluated the impact of cascade hydropower development on the river landscape. Chen et al. 17 adopted the West–East gas pipeline project in China as the research object, and studied its impact on the landscape pattern by comparing the changes of landscape indicators along its route.

In terms of research on the impact of highway construction on the landscape pattern, Qianqian et al. 18 used remote sensing data to compare the pre- and post-construction stages in a 500-m buffer zone along the highway. Yong et al. 19 analysed a 15-km buffer zone on both sides of the Yuyi expressway (Chongqing section). With the help of ArcGIS technology and landscape ecology methods, these researchers explored gradient differences of land use and landscape pattern evolution. Keken et al. 20 monitored the land cover typologies in Czech Republic’s highways for nearly 60 years and studied the impact of road construction and operation on changes in landscape structure. Huang and Ting 21 studied the impact of road construction on landscape fragmentation and evaluated their influence and environmental variables. Mothorpe et al. 22 studied the impact of the American intercontinental highway construction on land use, and provided technical support for future agricultural land protection plans. Huang et al. 23 investigated Taiwan’s township roads and showed that road construction led to varying degrees of isolation and fragmentation on the overall landscape pattern.

The above researches provide an effective method for in-depth evaluation of the changes of regional landscape pattern in the process of engineering construction, but it does not consider the vulnerability and particularity of plateau landscape under extremely complex geological conditions. Therefore, with the help of the above research methods, it is necessary to evaluate the evolution characteristics of landscape pattern in the process of highway construction in high-altitude Plateau, and the research results can provide differentiated governance schemes for landscape restoration in similar regions.

The impact of infrastructure construction on ecological risks

With the rapid development of infrastructure development, the ecological impact of these works has caused higher stress levels in the surrounding environment. Many Chinese scholars have focused on theory and methods development of landscape ecological risks assessment. Consequently, a research framework of landscape ecological risk assessment has already been developed.

However, the landscape factors that have been considered are mainly focused on the quantification and analysis of landscape patterns. These research results have generally been used to characterize landscape ecological risks indirectly, though. In this vein, different scholars have selected relevant indicators, methods and models and applied them to different regions and for different evaluation purposes. For example, Jian et al. 24 proposed environmental protection measures for tunnel construction by evaluating the ecological risks and provided a reference for the construction of similar geotechnical projects. Wang 25 studied the ecological environment along a high-speed railway project and put forward several measures to prevent its disturbance. He and Xiong 26 selected several indicators and analyzed their impact on the ecological environment with the aim of reducing ecological risks. Jing et al. 27 verified the impact of the construction of a cross-sea bridge on the water quality and ecological environment of the surrounding sea. Jianhua et al. 28 used multi-scale and multi-source remote sensing data to monitor and analyze the ecological environment and socio-economic impacts along a railway project. Fang 29 analyzed the main ecological environmental problems and the main influencing factors of metal mine construction projects. Then, some methods for ecological environment assessment were proposed for those mine projects.

In terms of the impact of highway construction on ecological risks, many researchers have also adopted different study perspectives (from micro and macro frameworks, and from single roads to whole road networks). Bian et al. 30 assessed the ecological and health risks of the surrounding areas of a highway project by analyzing the persistence of heavy metals in the soil. Limin et al. 31 quantitatively studied the gradient changes of the landscape pattern in different buffer zones of roads according to an ad-hoc landscape pattern index. Liang and Nianlai 32 adopted an index evaluation method to analyze the impact of highway construction on the surrounding ecological environment. Specifically, they focused on how to alleviate the impact of highway construction to maintain the sustainable use of local natural resources. Ting and Zongmin 33 proposed an ecological environment impact assessment index system for highway construction. And more recently, Igondova et al. 34 proposed another ecological impact assessment method for roads construction. In this case, though, their method included an ecological risk assessment index which could be applied in quantitative research studies.

Research on landscape ecological risk assessment methods

Landscape ecological risk assessment frequently encompasses two methods: those based on risk sources, and those based on sink and landscape patterns. The so-called early landscape ecological risk assessment is one of the former methods, but it is not applicable when the regional ecological stress factors are not clear 35 , 36 . Evaluation methods based on landscape patterns directly evaluate landscape ecological risks from spatial patterns on a regional scale. In this regard, ecological risk assessment methods based on land use and cover changes have become a research hotspot 37 .

In the process of ecological risk assessment, the construction of landscape ecological risk indices based on landscape patterns or land mosaic patterns have become prominent too 38 . The risk level of the landscape can also be measured from the existence of external forces (i.e. rapid urbanization) and the internal pressure capacity of the landscape 39 . In terms of evaluation indicators, the expert scoring method is frequently used as research method. The sorting normalization method is also used as well, but the assignment method is conditioned by some subjective weight normalization which can affect the eventual assessment of different evaluation indicators 40 , 41 . As a systematic method, a multi criteria decision making (MCDM) method can reduce the interference of decision-makers' subjective judgments on decision-making, and is widely used in various fields of ecological risk assessment 42 , 43 . Among the MCDM methods, the analytic hierarchy process (AHP) method and the technique for order preferences by similarity to ideal solutions (TOPSIS) method are commonly used methods. Peng et al. 44 used AHP to determine the weight of each factor affecting wetland ecological risk, and established a risk assessment model. Based on the improved AHP, Zhang et al. 45 combined the fuzzy comprehensive evaluation (FCE) method and applied the improved AHP to the ecological environment impact assessment of expressways, and concluded that the improved method has good objectivity and reliability, applicability. Luan et al. 46 used TOPSIS method to conduct multi criteria decision making analysis on environmental pollution caused by land cover change, and the research results provided scientific guidance for regional environmental management and planning. In addition, the combination of AHP and TOPSIS method has become an important method for ecological risk assessment 47 , 48 .

Recently, landscape ecological risk assessment methods usually accommodate multi-element ecological risk assessment. However, the selection of evaluation indicators remains subjective. Also, there is a lack of quantitative standards for ecological risks. Most of the evaluations are based on qualitative analysis, whereas quantitative analysis still is in an exploratory stage. Consequently, a comprehensive ecological risk assessment system needs to be established as soon as possible. Namely, a standard method for ecological risk assessment should be determined to provide a theoretical basis for further ecological environmental management and risk prevention measures. This method will be applied to highway construction projects, unlike many prior studies which have focused on cities or river basins.

Research area and research methods

General situation of the study area.

The extensive repair work for the highway maintenance of the Qumei to Gangba highway section commenced in early 2019. This highway is located in the southeast city of Shigatse, in the Chinese Tibet autonomous region. The route is 145.64 km long and runs through three counties and seven towns. The terrain has a high altitude in the middle and low altitude at both ends. The highway runs along riverbed terraces and at the foot of mountain slopes. The average altitude of the highway is around 3850–4750 m above sea level.

The geomorphology of the area where most of the highway is located is a plateau valley landform with some mid-mountain landforms. Wide river valley areas are mostly U-shaped valleys and narrow V-shaped river valleys. The landforms are mainly developed as modern riverbeds, floodplains and river terraces accumulating and cutting out structural landforms. High terraces are generally developed in the valley area, with high mountains on both sides of the valley and steep slopes. The main source of replenishment for the river is rainfall water. Most of the highway track is located at the foot of the mountain slope where the topography is relatively even.

The area where the route is located has a continental climate. The main features are dryness and lower levels of oxygen due to altitude, sufficient sunshine, and a wide temperature difference between day and night. Meteorological records from the Chinese Meteorological Bureau from recent years show that the annual average temperature of Sajia County is 5.5 °C, and the annual precipitation is 35 mm. The Sangzizhu area has a dry climate and thin air, with an average annual temperature of 4.9 °C and an average annual rainfall of 430 mm. The terrain of Gamba County is complex with large vertical changes. The highest point is 6783 m above sea level, whereas the lowest is 4375 m above sea level (a height difference of 2.408 m), and the annual average temperature is 1.5 °C. We used ArcGIS software to extract the vector boundary of some counties in Shigatse region, and superimposed the vector data of highway from Qumei to Gamba in the software. Finally, we got the location of the highway (Fig.  1 ).

Location of the highway section from Qumei to Gamba.

Model construction

Buffer analysis.

A highway is a linear structure, and its impact on the ecological environment is generally located on both sides of the highway. Buffer analysis is an analysis tool for studying the ecological and environmental effects of roads and other linear structures. Based on this method, the spatial variation of the impact of roads on a certain indicator can be studied by comparing the spatial differences of related indicators in buffer zones. This method has been widely used in the fields of ecology and pollution evaluation 49 . Like previous research, on the basis of Fig.  1 , this study uses the highway as the baseline and uses the buffer analysis tool built in ArcGIS10.8 to generate a 10-km buffer zone on both sides. The influence process of highway construction on the landscape pattern and ecological risk is discussed indirectly by using the differences measured in each index and buffer zones (see Fig.  2 ).

10-km buffer zone of the highway from Qumei to Gamba.

Construction of the ecological risk index

Ecological risk refers to the probability of which a regional ecosystem can remain stable in response to external disturbances (including natural and/or human activities). A landscape ecological risk model is a method that quantifies ecological risks by considering internal and external factors of the ecosystem. Hence, an ecological risk index model evaluates the ecological risk status of a region from the landscape characteristics such as the landscape disturbance index that characterizes the degree of external disturbance. It also uses the landscape vulnerability index to describe the ability of the ecosystem to maintain stability.

The landscape ecological risk index along the highway is composed of two parts, namely the landscape disturbance index and landscape vulnerability index. The Landscape disturbance index ( E i ) includes three factors: the landscape fragmentation index ( C i ), the landscape separation index ( S i ) and the landscape dominance index ( Di ). These indices are weighted to interpret some system results.

Landscape disturbance index ( E i )

The landscape disturbance index can reflect the disturbance degree of different landscape types. We select here a landscape fragmentation index, landscape separation index and landscape dominance index to build the Landscape disturbance index 50 .

Landscape fragmentation index ( C i )

Landscape fragmentation is a process in which the regional landscape structure gradually tends to be complex, heterogeneous and discontinuous under the action of external factors. The landscape fragmentation index has important ecological significance in measuring the loss of biodiversity. This index is calculated as:

In ( 1 ), n i is the number of patches of landscape type i and A i is the total area of landscape type i .

Landscape separation index ( S i )

The landscape separation index is used to describe the degree of separation between landscape patches and quantify the connection between various ecosystems. Its expression is:

In ( 2 ), A is the total area of the landscape.

Landscape dominance index ( D i )

The degree of landscape dominance is the difference in the areas of various patches. It describes the degree in which the overall landscape is dominated by the main landscape types. The greater the landscape dominance grows (or falls), the higher the increase (decrease) in area ratio differences of every landscape type. The calculation formula is:

In ( 3 ), R i is the ratio of the number of risk evaluation units with landscape type i respect to the total number of risk evaluation units; F i is the ratio of the number of patches with landscape type i to the total number of patches in the evaluation unit; L i is the ratio of the area of landscape type i to the total area of the evaluation unit.

After calculating the three indices of C i , S i and D i according to formulae ( 1 )–( 3 ), a normalization has to be performed. This research assigns the following weights for the fragmentation, separation and dominance indices: 0.5, 0.3, and 0.2, respectively. The calculation formula of the landscape disturbance index for each land cover type of each risk assessment unit is obtained as follows:

Landscape vulnerability index ( F i )

The landscape vulnerability index refers to the ability of the regional ecosystem to resist external disturbances. This is the internal factor that characterizes ecological risks. This index is closely related to the stage of the landscape in the process of natural alternation. According to classification of land uses, the landscape of the study is divided into six main types: cultivated land, wood land, grass land, water land, construction land, and unused land. Also, based on previous studies and the actual conditions of the Tibet region (Yanxu et al. 2015; Qiran and Hui 2014), the expert scoring method is used to determine the value of the landscape vulnerability along the highway. The value of landscape vulnerability index for each land type is deemed as follows: unused land is 6, water land is 5, cultivated land is 4, grass land is 3, wood land is 2, construction land is 1, and then normalized to obtain various types of landscape vulnerability indexes. The values above then become 0.2857, 0.2381, 0.1905, 0.1429, 0.0952, and 0.0476, respectively.

Landscape ecology risk index ( ERI k )

In order to connect the internal composition of the landscape pattern with the regional ecological risk status, and combine it with the above indices, this study uses the area proportions of each landscape type. This way, a landscape ecological risk index model is built. This index model can be used to describe the relative ecological properties loss of a certain sample area. The latter fully reflect the changes in ecological risks caused by the change of landscape pattern. The specific construction of the model is as follows:

In ( 5 ), ERI k is the ecological risk index value of the kth risk assessment unit; A i is the area of land cover type i in the kth risk assessment unit; A is the area of the kth risk assessment unit; E i and F i are the value of landscape disturbance index and landscape vulnerability index of land cover type i in the kth risk assessment unit, respectively.

Spatialization of ecological risks

Division of the evaluation unit

According to the scope of the study area and the sampling workload, we proposed a method for combining point grid evaluation units with the area vector evaluation units. If the grid is too large, it cannot reflect the spatial difference. If the size is too small, the landscape type is too single and the calculation is too large. Referring to previous studies, the grid should be 2–5 times of the average patch area 51 , 52 . Therefore, on the basis of considering the actual situation and workload of the research area, a square of 2 km × 2 km was used as the smallest area (unit) to calculate the landscape comprehensive index. The sampling method was equidistant for all squares. This process can be easily performed with ArcGIS. This way, 820 different risk units were analyzed, and the comprehensive ecological risk index was calculated for each of them. Then, the ecological risk level at the center point of each 2 × 2 km 2 sample area was used as the representative location of the index evaluation.

Spatial autocorrelation analysis

Global spatial autocorrelation is a description of the spatial characteristics interrelation of several attribute values of an entire region 53 . It is used to test whether the value of a spatial variable is related to the value of the same variable in the adjacent space. This study uses Moran's I coefficient to reflect the similarity of the attribute values of spatially adjacent regional units. The formula for calculating the global Moran’s I coefficient is:

In ( 6 ), W ij represents the spatial connection matrix between spatial unit i and j with i  ≠  j ; n is the total number of spatial units; X i is the attribute value of the spatial unit i ; X j is the attribute value of spatial unit j ; and \(\overline{X}\) is the attribute average value of all spatial units. The value of I ranges from − 1 to 1. When I  = 0, it means that the space autocorrelation is irrelevant. When I takes a positive (negative) value, there is a positive (negative) correlation.

Local spatial autocorrelation analysis can also be applied when: it is necessary to consider if there is a local spatial aggregation of high or low values of observations; when we want to find out which regional unit contributes more to the global spatial autocorrelation; and when we want to find to what extent the global assessment of the spatial autocorrelation conceals some abnormal local conditions. This study used the local autocorrelation LISA index to analyze the spatial aggregation of high and low values of the regional ecological risk index. Then, we explored the abnormal areas of the ecological risk distribution by identifying potential high or low “hot spots” with significant ecological risks. The formula for the LISA index is as follows:

Data source and processing

The basic data used in this study mostly encompassed remote sensing image data, vector data of the highway and administrative area, as well as land use and land cover data.

Remote sensing image data. The large and medium-level repair of the highway maintenance works on the Qumei to Gamba section of the highway took place between early 2019 and October 2020. In addition, because the characteristics of ground classes in high altitude areas of the plateau are often not obvious, the accuracy of selected training samples may not be guaranteed if low-resolution images are used, which may reduce the accuracy of classification. This study selected Sentinel-2 remote sensing images taken in October 2016, October 2018, and October 2020. The data was acquired from the European Space Agency’s (ESA) website with a resolution of 10 m. This raw source data went through some image processing such as orthorectification, registration, and band fusion. The preprocessed data as a data source for supervised classification (Table 1 ).

The vector data of the highway and administrative area. The administrative area data was mainly gathered from the administrative boundary data of the Shigatse area, including the administrative boundaries and administrative centers of the township-level regions. The vector data of the highway from Qumei to Gamba section of the highway was also collected. This data was sourced from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences ( https://www.resdc.cn/ ).

Land use and land cover data. With the support of ENVI5.3 software and ArcGIS10.8, a support vector machine algorithm was used to supervise and classify the preprocessed remote sensing images. The sentinel-2 image data of different phases were also automatically interpreted and the accuracy of the data remained always above 90% (Table 1 ). Finally, we took cultivated land, forest land, grassland, water area, construction land and unused land as the main landscape types in this study.

Analysis and results

Analysis of landscape pattern changes.

According to the land use of the study area and the six-type land classification of the Chinese Academy of Sciences (cultivated land, wood land, grass land, etc.), the overall landscape pattern of the study area was analyzed. It can be observed in Fig.  3 that grass land, wood land and unused land were the main types of land use. This is common because of the topography of the Tibetan plateau region. Construction land was mainly distributed in several towns and villages such as the Qumei township, and along the highway. Cultivated land was distributed in large extensions around residential areas. The area of water land was the smallest, mainly distributed on both sides of the highway.

Map of land usages in the study area from 2016 to 2020.

In order to fully show the influence law of highway construction on the landscape pattern, therefore, we estimated the land use transfer matrix along the highway in 2016–2018 and 2018–2020. By comparing the land use type transfer before and after highway construction, the influence of highway construction on landscape pattern was quantitatively evaluated. The calculation results are shown in Tables 2 and 3 respectively.

In the process of land type transfer, the main types of land type transfer are unused land to grassland (98.17 km 2 ) and grassland to forest (105.48 km 2 ). Unused land and grassland were turned out, accounting for 19.01% and 9.62% of their respective areas in 2016, respectively. Meanwhile, woodland, unused land and water area accounted for 20.19%, 13.85% and 13.80% of their respective areas in 2018, respectively. However, due to the small total area of water area, the converted area was less than 1 km 2 . It can be concluded from the transfer of land use types from 2016 to 2018 that the area of unused land was reduced before road construction, and the vegetation coverage was increased overall. Essentially, the overall transfer area of land use types was very small, and this transfer scenario was relatively simple.

It can be seen from Table 3 that the transfer of land use types is significantly more complicated after highway construction. The bidirectional transfer between grassland and unused land and between grassland and forest land is the main type of transfer. Among them, 203.50 km 2 of unused land was transferred from grassland, followed by 140.45 km 2 of unused land from grassland to woodland. Meanwhile, unused land and forest land were the main sources of grassland transfer, with 49.26 km 2 and 46.60 km 2 respectively, but their area was much smaller than that of grassland transfer.

Compared with the land use transfer before highway construction, it can also be seen that during highway construction, the transfer proportion of all land types increased significantly. The proportion of grassland and water area transferred out is more than 20%, and the area of water area, unused land and construction land transferred out account for 51.47%, 32.08% and 30.69% of the total area respectively.

According to the above analysis, it can be inferred that in the process of highway construction, grassland was damaged and polluted, and a large part of grassland was transferred to unused land, and the change of landscape pattern was more obvious than before highway construction, and the change of landscape pattern was more obvious after highway construction.

Due to the differences in the distribution of land use types, the landscape also had a significant change over time. As can be deduced, the major changes involved the grass land, unused land and wood land (Fig.  4 ). Initially, grassland was the most abundant landscape type, and it accounted for more than half of the total area of study. Wood land increased by 3.65% in 2016–2018, and by 3.27% in 2018–2020. Conversely, grass land decreased by 2.74% and 8.85% across the two periods analyzed. The unused land showed opposite trends in both periods (first a decrease of 1.08% before the highway construction, then an increase of 5.24% after the highway construction).

Changes in various landscapes (land use types) along the highway in 2016–2018 and 2018–2020.

In order to study the changes of landscape patterns quantitatively, we calculated the landscape ecological risk index with Fragstats4.2 software (see Table 4 ). The landscape indices of different landscape (land use) types also showed significant changes. Before and after the highway was constructed, the ecological risk index of cultivated land and construction land was the smallest. The reason was that the distribution of cultivated land and construction land was relatively concentrated, so the integrity of the patch improved. Unused land was highly fragmented and had the highest risk index, which was mainly related to the nature of land use in the landscape type. Combining the landscape indices of various landscapes, the ecological risk index along the highway in 2016, 2018, and 2020 was also calculated (with Formula 5 ). After its calculation, the ecological risk index of the three phases were 0.2316, 0.2217, 0.2822, respectively. Judging from the overall changes in the four years of the time span, the ecological risk index of the buffer zone did not change significantly before the highway was constructed. However, afterwards, the ecological risk index increased much more than in the previous period (a differential increment of 0.0605 and total increase of 27.29%). This variation indicated that the ecological risk index along the route showed a steeper upward trend after the highway was constructed.

Analysis of the temporal and spatial changes of the ecological risk index

Distribution characteristics of ecological risk index.

Based on the grid model and the Kriging interpolation method performed with the geostatistical analyst module of ArcGIS software, the ecological risk index of each risk assessment unit was spatially interpolated. This allowed us to create the ecological risk index spatial distribution map along the highway (see Fig.  5 ).

The distribution of ecological risk index along the highway in 2016, 2018, and 2020.

In order to better discriminate the ecological risk changes in the areas along the highway and further study the impact of project construction, we created five ecological risk index value bins, namely: low risk area (0 ≤ ERI < 0.2), sub-low risk area 0.2 ≤ ERI < 0.4), medium risk area (0.4 ≤ ERI < 0.6), sub-high risk area (0.6 ≤ ERI < 0.8) and high risk area (0.8 ≤ ERI ≤ 1.0). An ecological risk level distribution map was drafted (see Fig.  6 ). It can be seen therefore that during the construction of the highway, the ecological risk level changed significantly. This was mainly manifested as a generalized shift from a lower level of risk to a higher level of risk in most areas.

Distribution of ecological risk levels in 2016, 2018, and 2020.

Time series changes of the ecological risk index

In Table 5 , the analysis highlights that the proportion of area occupied by the low risk area was the largest in the three years of the study (51.35%, 57.71%, and 36.65%, respectively). The area of low risk increased slightly before highway construction, then decreased sharply during the construction period (a decrease of 21.07%). The area change of the medium risk area was in second place (it increased by 10.58% during the construction of the highway). The area of high risk had the smallest proportion, but the maximum area appears in 2020, when reached the 2.76% of the total area. The sub-high risk area had the smallest amount of change in the three phases (area changes remained below 1% in the two periods). In general, before the road was constructed, areas with low ecological risk increased, while areas with high ecological risk slightly decreased. However, after the highway was constructed, the opposite change occurred. This indicated that the ecological security status during the construction of the highway affected the surrounding area to some extent.

Based on the previous classification of ecological risk grades, the analysis of ecological risk grade transfer changes helps to identify further local differences. Hence, with the help of a spatial statistical analysis method, the ecological risk transfer matrix of the 10-km buffer zone in two periods could be calculated (see Tables 6 , 7 ).

In Table 6 we can see that the changes in the ecological risk exhibited the characteristics of a higher level of risk shifting to adjacent lower risks from 2016 to 2018. The total area of the reduced ecological risk level area was 250.85 km 2 , while the area with increased ecological risk level was just 0.26 km 2 . This indicates that the ecological security situation along the highway improved during this period. In the process of ecological risk transfer and transformation encompassing all levels, the dominant area transfer occurred from sub-low risk to low risk, with an area of 183.83 km 2 accounting for 73.21% of the total transferred area. Overall, it can be observed that the ecological environment along the highway was generally better before the road was constructed.

In Table 7 we can see that the changes in ecological risk grade between 2018 and 2020 were still dominated by the transfer of adjacent risk levels. In this time span, the area of transfer across risk levels was minimal. During the period of highway construction, the ecological risk changes were mainly based on the transfer of low-level risks to adjacent high-risks. The total area of the areas with elevated risk levels was 1071.34 km 2 , which accounted for 37.10% of the total area. The area with reduced risk levels was 12.47 km 2 , which was far less than the total area with increased risk. These results highlight that the overall ecological risk along the road rose during the construction of the highway and the overall ecological security situation showed a downward trend.

Spatial autocorrelation analysis of the ecological risk index

Global spatial autocorrelation analysis.

With the aid of the “Geoda” software, we calculated the global Moran’s I index value of the ecological risk index in 820 2 × 2 km 2 sample areas along the highway in 2016, 2018 and 2020. Namely, this calculation was used to verify the spatial pattern and significance of the ecological risk index of the entire study area.

The Moran’s I index in 2016, 2018 and 2020 was 0.954, 0.952 and 0.955, respectively. The index in the three phases was also positive, and its change trend not very obvious. However, this indicates that the ecological risk index of the area along the highway had a moderately strong positive correlation with the spatial distribution (i.e., adjacent plots had mutual influence and showed a high degree of spatial similarity). In the time series, the spatial clustering of plots with similar land use showed first a trend of ecological index decrease, and then, an increase. This indicates that the overall spatial differentiation of the landscape ecological risk intensity increased slightly along the route during the construction of the highway.

Local spatial autocorrelation analysis

The global autocorrelation analysis mainly considers the overall distribution of the ecological risk index. However, the local spatial autocorrelation helps to discriminate more fine-grained (local) change characteristics and spatial patterns. The LISA graph of local autocorrelation was used in this case to analyze the ecological index risk plots around the highway. Rook's adjacency weight matrix was also used to calculate the local autocorrelation results along the highway from 2016 to 2020 (see Fig.  7 ).

Local spatial autocorrelation diagrams of the ecological risk index.

It can be observed in Fig.  7 that the types of the spatial distribution of the ecological risk index in the three phases was mainly of high–high aggregation and low–low aggregation. The low–low aggregation areas of ecological risk were mainly distributed in the northern and southeastern parts of the study area. This indicates that the ecological risk intensity in this region was low, and the ecological risk intensity of adjacent areas was also low. The latter may have happened because the area had more wood land and this ecosystem was more stable. The high–high aggregation areas of ecological risk were mainly concentrated in the central and southern part of the study area. This highlights that the ecological risk grade of these areas was high, and the ecological risk grade of adjacent areas was also high. This probably happened because the area had more unused land and low vegetation coverage. Finally, it can also be seen that the number of high–high concentration plots increased significantly from 2018 to 2020. This may have happened because grass land was extensively destroyed during the highway construction process, which further increased the area of unused land.

At present, studies on the impact of engineering construction on ecological risk are mainly focused on large-scale hydropower projects and urban roads in developed areas, while there is still a lack of systematic research on ecological risk assessment in high-altitude Plateaus. The process which highways impact the ecological environment is quite complex. This study has adopted the macro perspective of the landscape pattern and enabled the development of an ecological risk model. According to a comparison of the risk changes in two stages (before and after the highway construction), the impact of highway construction on the ecological environment has been quantified. Although there are certain limitations in this study, the analysis results are consistent with the actual survey situation for the highway.

Regarding extant research on the impact of highway construction on the ecological environment, most researchers have focused on time-based studies over an extended time period 20 , 54 . The research reported here adopted the time period corresponding to the highway works maintenance program. Remote sensing data analyzed in the study were selected from the same month of each year with a unified time scale. Furthermore, the landscape pattern and ecological risk indicators before and after highway construction were compared to study the impact of highway construction on the ecological environment. At the same time, due to the complex terrain in Tibet and the abrupt land features, in order to improve the classification accuracy, Sentinel-2 remote sensing images with a resolution of 10 m were selected. These images were combined with actual field surveys and, in some cases, incoherent data were corrected.

The analysis of landscape pattern before and after highway construction identified that the landscape pattern changes were more pronounced during the construction period than before construction. However, the overall landscape pattern as well as the types of main land uses along the highway did not change. This is in line with the conclusions of Gang, HuiJun, and Guang 55 on wetlands in arid areas, and Haihang et al.’s 56 on urban areas. The analysis of the ecological risk index before the highway construction scenario highlights that the average ecological risk index of the constructed highway is significantly higher than before. Although this is similar to the research study of Shiliang et al. 57 , it is fundamentally different to the conclusions of other researchers like Mo et al. 52 . A likely cause of this difference may be that the impact of construction on ecological risks of urban roads is different at the county-level and township roads, that is, urban areas may have more diversified risk sources.

For the convenience of discussion, we compared the differences between the relevant references and this study in methods, locations and main results in the form of tables (Table 8 ). It can be seen that some scholars have analyzed the ecological risk changes of river basins by establishing ecological risk models. The results show that in the process of urbanization, the ecological environment quality of these areas is declining, which is similar to the research conclusion of this paper 58 , 59 . Yuan et al. 58 also analyzed the relationship between flood feature values and landscape patterns through multiple linear regression methods, which provided useful information for regional landscape planning and watershed flood control planning. Different from the above references, this study quantitatively studies the changes of various landscape indicators during highway construction, which can provide specific reference for regional landscape governance. At the same time, Research on the impact of infrastructure construction on ecological risk levels identifies that the transfer of ecological risks mostly involved the transfer between adjacent risk grades (i.e., cross-risk transfer rarely occurred). This is similar to the conclusions of Jie et al. 40 on the ecological risks of the entire Qinghai-Tibet Plateau landscape. Furthermore, the research on the spatial heterogeneity identified that the ecological risk index of the three phases along the highway had a high degree of positive correlation with the spatial distribution. This spatial distribution was mainly of high-high aggregation and low-low aggregation. Particularly, the area with high-high aggregation increased significantly during the construction period. This is similar to the spatial distribution characteristics of the ecological risk index obtained by Fengjiao and Xiao 60 and Xie et al. 59 . However, their research objects and methods were quite different.

Relevant references also reported changes in regional landscape patterns and ecological risks caused by highway construction, which is similar to the method used in this study 52 , 61 . By analyzing the changes in the landscape pattern, these two references concluded that the landscape type with the largest area increase was construction land, while the area of unused land increased the most in the study. This may be caused by the large differences in population, economy, topography and other factors in the study area, which is located at extremely high altitude. It is worth mentioning that the highway is a single linear project in this study, so the research method of buffer zone is also adopted. Oliveira et al. 62 considered social and environmental factors, and used buffer and landscape index analysis to analyze the changes in the landscape pattern of the Rio Doce State Park in Brazil. They believed that the substantial reduction of farmland area should be given enough attention. Different from this reference, this study established a landscape ecological risk model and studied the aggregation effect of ecological risks through autocorrelation analysis. In addition, although some literatures do not establish ecological risk models, they take other factors such as social and economic into consideration. Through regression analysis, they studied the relationship between landscape pattern and socio-economic factors qualitatively or quantitatively, which had a certain positive effect on the development of ecological economy 63 , 64 , and is also some of the shortcomings of this study. By synthesizing social, historical, economic, environmental and other factors, constructing an ecological risk assessment model under the influence of multiple factors, thus laying the foundation for the establishment of a scientific and practical ecological development model, may be a valuable future research direction.

Changes in the landscape pattern of land use will inevitably lead to changes in regional ecological functions. Therefore, studying ecological risk changes from the perspective of its landscape structure helps to objectively reflect the ecological risk pattern of highways (and probably other linear infrastructures as well).

Conclusions

Combining the information of land use and land cover data with a Chinese highway vector data, this research study has evaluated the impact of highway construction on the landscape. As a result, an ecological risk model for a high-altitude plateau area has been proposed using a geographic information system combined with statistical analysis. The following conclusions are obtained from the Qumei to Gangba highway section (Tibet autonomous region), where this model has been implemented:

From 2016 to 2020, the major land use type in the buffer zone along the highway was grassland, accounting for more than 52% of the surrounding area. This land use was followed by woodlands and unused lands. The overall trend of land use change was a decrease in grassland area. Conversely, the area of woodland, cultivated land, construction land and water-based land increased. The type of transfer mainly involved the conversion of grassland to woodland, and a balanced mutual conversion between grassland and unused land. This means that before the highway construction, the area of grassland and unused land transferred to each other was almost the same. However, after highway construction, the area of grassland converted to unused land was comparatively much larger, whereas he mutual transfer area of other land use types was negligible.

The average values of the ecological risk indices at 2016, 2018 and 2020 were 0.2316, 0.2217 and 0.2822. This indicates that the ecological risk intensity of the study area also showed different trends before and after the highway construction. Before the highway construction, the ecological risk slightly reduced. Whereas after the road construction, the ecological risk increased significantly.

During the study period, the transfer of ecological risks mainly involved transfers between adjacent risk levels (cross-risk transfers seldom occurred). However, before the highway construction, the ecological risks were mainly transferred to the lower-level risk level. After the construction project, the ecological risks were mainly transferred to a higher risk level.

Global autocorrelation analysis performed with the Moran index indicated that the ecological risk index along the highway had a high degree of positive correlation with the spatial distribution. In other words, adjacent land uses interacted with each other and showed a high degree of spatial similarity. In terms of time series, the spatial agglomeration of land plots with similar ecological risk levels did not show an obvious trend. Based on the local autocorrelation analysis, though, it was found that the spatial distribution of ecological risk index was dominated by high–high aggregation and low–low aggregation. The areas of high-high aggregation increased significantly during the highway construction period and this evidences that the highway construction had an important influence on the spatial distribution of ecological risks.

This study has qualitatively and quantitatively analyzed the process and ecological effects of highway construction on the ecological environment from the perspective of the landscape pattern. It has also provided some new approaches for the study of infrastructure-related ecological risks in high-altitude plateaus. The study has mainly considered the impact assessment of a single highway, but have neglected other roads along the route. Similarly, the corresponding relationship between different buffer distances and landscape ecological risk has not been discussed. Those simplifications will be analysed in further research work, but we find it is highly unlikely that they could have affected the major conclusions from this study.

Due to the fragility of the plateau landscape in Tibet and the complexity of the geological conditions, how to carry out a quantitative analysis of the impact of construction roads on the ecological environment for this type of area has become the key to ecological environmental assessment. This study constructed a plateau high-altitude landscape ecological risk assessment model and analyzed the impact of highway construction on ecological risks dynamically. Its research results provided technical support for the construction of differentiated ecological restoration programs, and also provided a new research perspective and research foundation for ecological risk assessment of engineering construction projects in high-altitude plateau.

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Acknowledgements

This research is supported by the Branch of China Road and Bridge Corporation (Cambodia) Technology Development Project (No.2020-zlkj-04); National Social Science Fund Projects (No.20BJY010); National Social Science Fund Post-financing Projects (No.19FJYB017); Sichuan-Tibet Railway Major Fundamental Science Problems Special Fund (No.71942006); Qinghai Natural Science Foundation (No. 2020-JY-736); List of Key Science and Technology Projects in China’s Transportation Industry in 2018-International Science and Technology Cooperation Project (Nos. 2018-GH-006 and 2019-MS5-100); Emerging Engineering Education Research and Practice Project of Ministry of Education of China (No. E-GKRWJC20202914); Shaanxi Social Science Fund (No. 2017S004); Xi’an Construction Science and Technology Planning Project (Nos. SZJJ201915 and SZJJ201916); Shaanxi Province Higher Education Teaching Reform Project (No. 19BZ016); Fundamental Research for Funds for the Central Universities (Humanities and Social Sciences), Chang’an University (Nos. 300102239616, 300102281669 and 300102231641).

The Branch of China Road and Bridge Corporation (Cambodia) Technology Development Project (No. 2020-zlkj-04), National Social Science Fund Projects (No. 20BJY010), National Social Science Fund Post-financing Projects (No. 19FJYB017), Sichuan-Tibet Railway Major Fundamental Science Problems Special Fund (No. 71942006), Qinghai Natural Science Foundation (No. 2020-JY-736), List of Key Science and Technology Projects in China’s Transportation Industry in 2018-International Science and Technology Cooperation Project (Nos. 2018-GH-006 and 2019-MS5-100), Emerging Engineering Education Research and Practice Project of Ministry of Education of China (No. E-GKRWJC20202914), Shaanxi Social Science Fund (No. 2017S004), Xi’an Construction Science and Technology Planning Project (Nos. SZJJ201915 and SZJJ201916), Shaanxi Province Higher Education Teaching Reform Project (No. 19BZ016), Fundamental Research for Funds for the Central Universities (Humanities and Social Sciences), Chang’an University (Nos. 300102239616, 300102281669 and 300102231641).

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C.L. wrote the main content of the manuscript. J.Z. determined the research content and research method of the manuscript. S.P.P. modified the grammatical structure of the manuscript. X.Y. did the calculations for part of the manuscript. Z.D. finished the data collection. J.H. made important revisions to the manuscript. P.B.-P. helped perform the analysis with constructive discussions.

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Li, C., Zhang, J., Philbin, S.P. et al. Evaluating the impact of highway construction projects on landscape ecological risks in high altitude plateaus. Sci Rep 12 , 5170 (2022). https://doi.org/10.1038/s41598-022-08788-8

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Construction projects typically involve a high degree of complexity. In the theory, complexity is understood as the number and heterogeneity of different interrelated elements ( Burke and Morley, 2016 ). Taking into consideration that construction projects 1) involve a high number of stakeholders, are 2) subject of frequent change orders, 3) of a long-term nature, and 4) affected by various influencing factors such as location, inflation, schedule targets, constrained budget, etc., it is difficult to understand, foresee and keep the under control ( Vidal et al., 2011 ; Vařbuchta et al., 2017 ; Kermanshachi and Safapour, 2019 ). In order to cope with the project complexity, the project itself should be supported with good-quality technical, economic, and contractual documentation. Unfortunately, many projects suffer from disputes between the parties involved, leading up to adverse consequences such as significant delays, loss of quality in follow-up work, or lack of future cooperation ( Lu et al., 2015 ). To imagine, National Construction Contracts and Law Survey reported that 30 percent of companies in the Great Britain had been involved in at least one dispute in the previous 12 months ( RIBA Enterprises, 2013 ). Such disputes are often caused by a misunderstanding of the contractual terms and conditions as well as violation of obligations, no matter if real or only perceived by one of the parties. Given regular occurrence of disputes in construction projects, good negotiating skills are becoming crucial ( Chow et al., 2012 ).

Research problem and aim of the paper

This paper deals with contract management problems and their influence on the construction and operation of a private road. Despite the fact that a wide body of knowledge on causes of claims, dispute resolution techniques, time and costs overruns, and project performance in general is available, there is a lack of studies that monitor these issues from a long-term perspective. In the case of the construction industry, this is essential because the duration of the life cycle of buildings is expected to be in the tens of years. At the same time, it is necessary to be aware that sooner or later the consequences of the decisions made in the early stages of the project will be felt. Although we know what the most common causes of disputes are and how they can be resolved, it is usually unclear to what extent the consequences of failing to resolve the dispute may increase in the future.

Accordingly, this study aims to investigate causes and effects of dispute in different phases of the life-cycle of a building facility. Therefore, the study addresses how contract management problems resulting from insufficient project preparation may affect not just the construction phase, but also the operation phase of the building facility’s life cycle. The single-case study analysed in this research points out how a long-term dispute can negatively affect the operation of an owner’s business; how it, for many years, has been tying contractor to a project which, from his point of view, had already been finished long ago; and especially how the continuously growing complexity of the problem significantly complicates its resolution.

Literature review

Project management literature provides a substantial body of knowledge concerning the assessment of a construction project’s success. The main success criteria are time, cost, and quality, also known as the “iron triangle” ( Ljevo et al., 2017 ). These criteria usually act as contradictive objectives and can be considered as interdependent parameters in a building project ( Hu and He, 2014 ). This mutual interdependency was analysed by many researchers, e.g. in terms of time and cost for water supply systems ( Zujo et al., 2017 ) and the balance of cost, time and quality for reinforced concrete ( Hosseini et al., 2017 ). The aspect of project cash flow is also crucial as it allows the assessment of working capital requirements ( Maravas and Pantouvakis, 2012 ).

The above-mentioned factors are crucial in construction disputes as well. Ilter (2012) deals with three categories of disputes, namely: extension of time, payments, and quality of works clearly corresponding to the iron triangle. Furthermore, study of Ilter (2012) identifies the relations between dispute categories with dispute factors such as late instructions by the employer, inadequate/incomplete specifications or unclear contractual terms. It should be emphasised that the contract is among the most important components of the construction project ( Safa et al., 2017 ) and project documentation forms an integral part of the contract. This importance is documented by Leśniak et al. (2018) , who point out that problems with project documentation are among the main causes of delays in both Poland and Slovakia. Disputes may arise for various reasons, in addition to usual ones between client and contractor such as cost and time overruns for ongoing projects ( Nahidi et al., 2017 ; Johnson and Babu, 2020 ; Shoar et al., 2022 ; Subramanya et al., 2022 ), disputes may also be related to inadequate cost estimations for repairing damages caused by large scale natural disasters, such as floods or windstorms ( Hanak and Korytarova, 2014 ).

Construction disputes may be destructive for projects ( Ilter and Dikbas, 2009 ), therefore efforts should be made to prevent them in the first place. This issue is highly topical within the research community; e.g., Lee et al. (2016) found 140 articles dealing with dispute prevention. In this relation, Molenaar et al. (2000) developed a structural equation model for predicting of construction contract disputes. As presented by Naji et al. (2020) , structural equation modelling enables to determine a regression model for the dispute occurrence as an output variable in terms of the dispute causes as real variable. However, since it is not possible to prevent all disputes, it is subsequently necessary to resolve them. In the case of systematic violations of one party’s obligations, a judicial decision is typically needed ( Yaskova and Zaitseva, 2017 ). This solution is commonplace in modern construction projects ( Biering et al., 2016 ); however, it may take long ( Dziadosz et al., 2015 )—disputes can drag on for many years, which is why parties also consider alternative dispute resolution (hereinafter referred as ADR) methods, an option that arose as an alternative to lengthy and costly processes of arbitration and litigation ( Cheung, 1999 ). The use of ADR (such as negotiation, arbitration or adjudication) depends on the nature of claims that are to be decided and also on the parties’ perception of fairness and outcomes of win and losses in claims ( Lee et al., 2016 ). The list of factors that affect the selection and use of ADR provided by Lee et al. (2016) is comprehensive, involving–apart from fairness and outcomes–e.g., bindingness, cost, confidentiality, control over the proceedings, lawyer’s influence, perception of risk, and complexity of disputes. Regarding related costs, disputes resolution carries explicit costs such as lawyer’s fees, court fees, consultant costs etc., however, other types of costs (so-called hidden costs) should also be considered, e.g., reduced project working efficiency, damaged reputation, or the aspect of future cooperation ( Lu et al., 2015 ).

The frequency of disputes occurrence was analysed by Ilter and Dikbas (2009) . They have revealed that design and build causes 8 precent more disputes in contrary to the build method and also that the frequency is positively correlated with the size of the contractor. According to Tazelaar and Snijders (2010) there is an 84 percent probability that at least some problem will occur within the project and 81 percent probability that the problem will be discussed successfully with the other party. However, if the problem involves a delayed payment or claim of damage, then there is 10 percent probability that the case will lead to arbitration or other legal steps.

The case study analysed in this paper deals with road construction. Road construction projects also involve the iron triangle; however, it should be noted that problems and risks are higher due to the duration and size of such projects, as well as unforeseen economic and other conditions such as material and energy prices. In the case of road construction, increased energy costs influence material costs and may slow down projects significantly ( Hashem Mehany and Guggemos, 2015 ). An example of the procedure of contractor’s claims management in a road works project is provided by Rybka et al. (2017) . Mishmish and El-Sayegh (2018) have reported causes of claims in road construction projects in the UAE. Based on their findings, the most likely causes consist in variations initiated by the client or engineer, delays caused by the contractor (e.g., as a consequence of lack of resources and machinery on the construction site), and inadequate site investigation before bidding.

Providing sufficient background materials for each dispute is crucial. Typically, contractors lack documents such as adequate photographic evidence, time sheets, site diaries and revised drawings to pursue their claims ( Vidogah and Ndekugri, 1998 ). Therefore, an experienced supervisor ( Kongsong and Pooworakulchai, 2018 ) as well as the use of modern technical devices and solutions on the construction site ( Banaszek et al., 2017 ; Acosta et al., 2019 ) are essential.

Materials and methods

The dispute analyzed in this case study was settled in court. Since authors of the paper participated in the preparation of the expert opinion for the court, they had at disposal complete documentation from the court file. Detailed documentation that has been available for the selected case study has been carefully studied. This documentation includes the following: contract for work, technical documents including initial road design, relevant documents provided by both parties to the dispute, and materials obtained during personal investigation of the site. All sensitive information and materials are published in an anonymised form (incl. location, time, name of the parties involved, the amount of costs, name of the technology used). The article contains only the facts that are relevant to the achievement of the paper’s objective, i.e., to identify dispute factors and the related consequences.

After examining the textual and graphical documentation in the court file (analysis no. 1), an additional set of information was obtained during personal investigation of the site that took place in 2018 (analysis no. 2). Due to the long duration of the dispute, 6 years passed before the revision expert opinion was ordered and processed. On the other hand, it enables to explore evidentiary material covering the whole six-year period. This investigation primarily involved physical documentation of visible damages, the extent of which progressively evolved over time (when compared to previous evidence available in the court file). Subsequently, the disputed matters were discussed with both parties (investor and supplier) in order to obtain the widest possible material for the subsequent assessment of responsibility for the occurrence of defects. When all the defects have been identified, the causes of problems were investigated. Then, cause-effect relations were assigned and graphically presented in multi-dimensional diagram. In addition, the entire analysis has been performed in the context of the iron triangle (i.e., time, cost, and quality issues). The case study research methodology is graphically presented in Figure 1 . Based on the findings, several lessons learnt are provided at the end of the manuscript.

FIGURE 1 . Case study research methodology.

Regarding scientific methods use, analytical-synthetic cognitive procedures have been applied to achieve an understanding of the qualitative data. Of these, a causal analysis was particularly important as it helps identify causes of phenomena. It is crucial to ensure objective and deep insight into the problem, especially in situations when observer just see certain process, but its manifestations are dependent on a very complex causal chain of causes and effects ( Molnar et al., 2012 , p. 22). For graphical interpretation for casual models, different types of casual diagrams are used, e.g., mind mapping and casual loop diagrams. Mind maps provides nonlinear graphical interpretation of data and casual loop diagrams are used to analyse qualitative data ( Milen et al., 1997 ). Generally speaking, casual diagrams help to predict how the system would respond to hypothetical interventions ( Pearl, 2000 ). For this reason, casual diagram was used as a graphical tool to present and understand causes and effects in analysed case study and, in the second stage, to facilitate the formulation of lessons learned. Project life cycle as well as the perspectives of both parties to the dispute in view of dispute categories are considered within the performed analysis.

Case study of a road construction project

The subject of the analysis consists in construction of a road on a privately-owned plot of land and its connection to the existing public asphalt road. Specifically, the road was to be used for transport to and from company premises. The road thus enables the entrance to the investor’s premises, parking and the connection of the public road to the storage hall. The investor needed to bring the premises into operation quickly and, for this reason, it began to consider a change in the originally envisaged technological solution for the road. The investor and the contractor reached an agreement where the contractor proposed to use a non-standard construction process which promised to shorten the construction period and reduce price. The technology lacked binding rules in the Czech Republic and there was no demonstrable experience with its use in other projects in the country. The basic characteristics of the project are provided in Table 1 and a cross-section drawing is shown in Figure 2 .

TABLE 1 . Basic characteristics of the analysed project.

FIGURE 2 . Cross section drawing of the road.

The investor and the contractor entered into a contract for work, but the contract unfortunately contained serious shortcomings consisting, e.g., in the absence of proper project documentation concerning the new technological solution attached to the contract as its integral part. All the above-mentioned facts subsequently led to disagreements and disputes between the contractual parties.

The road was built by the contractor, but defects and faults appeared during handover, resulting in the investor’s refusal to accept the road and its request for a repair of these defects and faults (consisting, e.g., of incorrectly implemented drainage). Although the work was neither officially accepted nor paid for, the investor started using it for its business activities, which involved heavy freight traffic on the road which had not been designed for that purpose. Consequently, the defects and faults deteriorated further, manifesting as faults on the road near the drainage sites, ruts in the covering layer, cracks and unevenness in the road and area deformations. Figures 3 – 5 show examples of defects on the analysed road and view on its structure.

FIGURE 3 . Cracks and deformations.

FIGURE 4 . Road surface irregularities.

FIGURE 5 . View of the road structure.

Since the parties were unable to agree on a solution to their dispute, they referred the matter to the court. As part of the investigation of the causes of the defects and faults and the liability for them, the following factors were identified: inadequate preparation of the ground plane, incorrectly implemented drainage, uneven thickness of the asphalt layer, partially insufficient compaction of the underlying structure, insufficient binding between the covering and the supporting layer, and overloading the road by heavy freight traffic.

The situation was further worsened by the increasing number and scope of faults as time progressed, the fact that there was no building diary, as well as the absence of as-built documentation. Selected most serious faults and defects were provisionally repaired by the investor on the spot, yet documentation was again not made with respect to these repairs. As a result, the entire dispute became fairly complex, especially with regard to the assignment of liability for the individual defects and faults.

Figure 6 provides a lucid two-dimensional overview of the above facts. The individual factors at play and their consequences are divided based on their time sequence into three life cycle stages, i.e., preparation, construction and operation. The second dimension consists in dispute categories (time, payment and quality). Colours indicate the decisions/activities/requirements of the parties in the dispute (blue = investor; orange = contractor; colour gradient = both).

FIGURE 6 . Causes and effects diagram.

The previous analysis showed that the project failure was caused by four main general factors: 1) selection of the contractor, 2) selection of the technology, 3) insufficient documentation, and 4) unsuitable use of the structure. The interaction of these factors made solving the dispute by agreement of the parties practically impossible.

The contractor selection process should generally be conducted in a way that results in implementation of the work by a construction company that has sufficient technical, managerial and economic qualifications to deliver the work in the required quality. Although the entire process of selecting the contractor was not part of the analysis, the authors note that a proper verification of the contractor’s qualifications is crucial in this regard. Only qualified contractors should be selected to implement the contract, which means contractors who are competitive, competent and capable ( Lam et al., 2010 ). A part of the qualifications should have consisted in documented (and verified by the investor) references proving that the contractor was competent and capable.

The above requirement for submission of references is also related to the selection of the technology to be used. The investor did not verify if other projects using this technology had been implemented in the Czech Republic. This fact is especially important since this construction technology was not tried and proven in the Czech Republic and lacked relevant generally binding standards.

The dispute escalated and was difficult to resolve particularly due to the absence of relevant documents. This means both design documents for construction and as-built documentation. Since the contract for work concluded in this particular case study did not contain the usual provisions concerning construction contracts and the investor was not experienced in this area, no building diary was made during the construction and the investor did not arrange for adequate supervision of the work. These factors significantly hindered the possibility for determining qualitative shortcomings of the construction, e.g., in relation to bad binding of the covering and supporting layers since these structures are not visible.

Finally, another important factor consisted in the fact that the investor started using the road and overloaded it with heavy freight vehicles for which the surface was not designed. This brought about a situation where both parties to the dispute bore some responsibility for the occurrence of certain defects and faults. For instance, as regards the road cracks, it is not clear to what extent they were caused by, e.g., insufficient drainage of the site as opposed to heavy traffic. Determining the exact degree of liability for the individual defects and faults is thus nearly technically impossible, taking into consideration the fact that the scope of the faults grew as the time progressed.

Finally, several lessons learnt from the analysis of the case study can be presented. Firstly, it is necessary to choose a technical solution suitable to the structure’s operational requirements. In this relation, a detailed project documents must be drawn up.

Secondly, the contractor selection process should take place on the basis of sufficient qualifications. The qualification requirements should enable a selection from a sufficient number of bidders with the aim of ensuring sufficient competition within the selection procedure and, simultaneously, exclude from the procedure such potential contractors that lack the qualifications for successful completion of the project or who have previously developed reference projects that had quality issues.

Thirdly, contractual provisions in the contract for work should be consulted with experts in construction contract management in order to minimise the possibility of disputes in the course of the project’s implementation and afterwards.

Fourthly, handover of the construction site to the contractor has to be properly recorded in order to ensure that the investor has met all its obligations as agreed in the contract for work.

Fifthly, during the course of the construction, the investor should arrange for oversight of the works by a supervisor; keeping a building diary including photographic documentation by the contractor is also necessary. These measures enable timely identification of low-quality work and achieving appropriate remedy; potentially, the same documents can serve as evidence for determination of the scope and liability for defects appearing during operation, which is especially important with regards to structures that are covered and cannot be visually inspected.

Sixthly, defects and shortcomings found during delivery of the completed work should be recorded with participation of both contractual parties and the supervisor. The defects and faults should be classified into two categories based on their severity, i.e., defects preventing the use of the work and defects not preventing the use of the work. This categorisation should be supported by the provisions of the contract for work; material defects preventing proper use of the work are generally considered to constitute grounds for non-acceptance of the work by the investor. A list of these lessons is provided in Table 2 showing the relationships between the problem, the measures adopted, and their desired effects.

TABLE 2 . Recommendations resulting from the case study analysis.

As a result, the impact of this dispute is negative mainly for the investor, but also for the supplier. For investor, the effects consist mainly in the limited possibility to use built communication and in additional incurred costs for carrying out local repairs. Since the supplier consistently and completely rejects its liability for defects, its main burden consists of attending court hearings and the related costs of legal representation.

This study aimed to identify dispute factors and the related consequences on a case study involving the construction of a road to commercial premises. The subject of the analysis involved a critical evaluation of the interaction of individual factors both in terms of the dispute categories and the life cycle of the project. The mutual interactions are depicted in casual diagram ( Figure 6 ), which shows the process in which a dispute becomes increasingly more complex and thus more difficult to resolve by reaching a mutually acceptable compromise solution.

Presented research is not without limitations. As this is a case study of one particular project, the observations and conclusions reached herein cannot be generalised to the entire construction sector. However, due to the high level of complexity of the analyzed case in the perspective of the life cycle, substantial implications can be derived not only for practice but also for theory.

This paper provides two main managerial implications. Firstly, the discussion of the case study brings several recommendations on how to avoid potential problems based on lessons learned from the situation that occurred in the studied project. Secondly, the conducted analysis shows that in case of protracted disputes arising from insufficient documentation, determining the party responsible for defects becomes extremely difficult.

From a theoretical perspective, performed analysis contributes to the current body of knowledge by highlighting potential undesirable growth in project complexity in relation to inexpert and controversial steps of the parties to the dispute. Furthermore, the findings presented extend our understanding of the long-term effects of contract management problems within individual stages of the project life cycle on both parties involved in the dispute and within iron triangle dimensions.

In order to reach generalisable findings in the area of transport construction projects, further project case studies should be analysed; in aggregate, they would have the potential to identify recurring negative scenarios, project inflection points and possibilities for general improvement in the area of contract management.

Data availability statement

The data analyzed in this study is subject to the following licenses/restrictions: business case of dispute. Requests to access these datasets should be directed to [email protected] .

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

This paper has been written with the support of a research grant FAST-S-22-7970 “Economic and managerial processes in civil engineering”.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: construction project, contract management, road construction, project documentation, dispute

Citation: Hanák T and Vítková E (2022) Causes and effects of contract management problems: Case study of road construction. Front. Built Environ. 8:1009944. doi: 10.3389/fbuil.2022.1009944

Received: 02 August 2022; Accepted: 26 September 2022; Published: 07 October 2022.

Reviewed by:

Copyright © 2022 Hanák and Vítková. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tomáš Hanák, [email protected]

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Road Construction Equipment Management: A Case Study on Selected Industry

2016, International Journal of Engineering Technology and Sciences

The major share of capital and equipment intensive operation goes to the road sector and the hydro – electric power projects. The construction sector in Ethiopia is developing at a fast rate and its capital budget is increasing from year to year. One of the reasons for this high growth is the number of new construction projects underway and those in the pipeline. In addition, the hydro-electric power projects the government has given a great emphasis to increase the current installed power of 780Mw to a total of 10,000Mw in the coming few years. These hydro-power projects are known for using a great deal of high investment heavy machineries. Therefore, the construction equipment management plays a great role in finalizing the projects with fewer budgets and no time over run. Considering the higher operation, maintenance and investment cost of construction equipment, it is important to have a carefully optimized decision making model that will help in the sizing and selection of the ...

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Construction Equipment Management Practice

Abstract The construction industry is a sector that relies on the use of construction equipment. Especially contractors who are engaged in road construction primarily demand high utilization of construction equipment. Construction equipment owned by contractors also constitutes the greatest portion of assets owned by them. To obtain the benefit from equipment and to utilize effectively these assets which have high economic value, contractors are required to have a well-developed system and guideline that directs equipment management practice in the right way. The practice followed by contractors in Ethiopia in the management of construction equipment is not being clearly known and studies regarding it are limited. However, problems related to construction management are increasing as the construction industry keeps on growing. Contractors are experiencing frequent downtime, less productivity but high operation cost and workplace accident related to construction equipment. The main objective of this research is thus to study the general equipment management practice followed by high-grade level contractors (from grade one up to three) who are engaged in road construction and identify significant causes of problems related to the management of construction equipment. A structured questionnaire was prepared and distributed for 52 grade one up to three contractors who are in the Addis Ababa region of Ethiopia. Out of these, responses of 33 contractors were obtained. The sample size is determined by using the kish equation for 94% confidence of interval. The collected data were analyzed using SPSS 16 statistical software package producing frequencies, percentages, and cross-tabulation charts. RII ranking is done to identify the most significant causes of equipment management problems. The finding obtained from this research indicates that only limited contractors (12.1%) have well-documented equipment management guideline. The practice of record-keeping of costs of operation and maintenance is not on the satisfactory level and preventive maintenance is also not the popular practice among contractors. Most of the time equipment maintenance is undertaken when it is breakdown. The Equipment management practice of contractors is highly influenced by the limitation of the financial capacity of contractors and most of the cause of the problem is due to inappropriate management practice they follow. A significant difference in the management of equipment is observed across grade levels; some grade one contractors who developed equipment management guidelines relatively have shown better equipment management practice.

Journal of emerging technologies and innovative research

Ghanasham Sarode

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Modern construction, characterized by effective and efficient utilization of construction equipment to accomplish numerous construction activities. Equipment may range from simple hand tools or apparatus to heavy-duty construction equipment. As a high level of productivity is important to any company’s success and survival, this paper help to fill the gap created by the absence of proper equipment management on the construction sites and helps to examine the overall performance of heavy-duty construction equipment. This study also examines the major contributing factors for productivity loss on the case study federal road construction projects to find important interventions for changes; this is because machinery management have high influence on productivity, cost and quality. Three federal roads where involved in this study, Namely, Mazoria-Durame-Hadero, Hadero-Durgi and Alaba-Alemgebeya-Hulbarek road projects in the southern regional state of Ethiopia. The assessment involved in this study was field observation and measurement to quantify downtime and the actual productivity and desk study to identify main factors contributing for productivity loss. In the entire study site, the OEE index show low percentage of productivity in between 55% to 68%. Out of the three primary component rates of OEE index only quality rate have a higher performance, which is about 90% and above. However, the performance rate in the entire study site shows lower performance(less than 76%). Due to this weak performance of the construction equipment productivity, the contractors forced to incur an additional cost from 1 – 2 % of the total monthly running cost allocated for the equipment. Out of the four main contributing factors for equipment productivity loss (machine factor, human factor, management factor and work factor) the management problem ranked first. Although there was low construction equipment productivity in the entire study site, the Resident Engineers (RE) do not have any compliant on the contractor regarding to their poor Performance.

jayaram sankar

International Journal of Advanced Technology in Engineering and Science (IJATES), www.ijates.com, ISSN (online): 2348 – 7550, Journal Impact Factor (2014): 1.012

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Purchasing with cash, financing through a loan, renting and leasing are four most common ways which are mainly affected by different construction equipment selection factors. For having the best result in profit for a construction industry choosing the best alternative for obtaining equipment is one of the most important issues. The optimum acquisition strategy comes from accurate estimates of revenues and cost and also some non-financial factors that effect on selection of appropriate equipment. The purpose of this paper is to evaluate these factors. The identification of the factors that effect on equipment selection has been carried out using different literatures and by interviewing from experts in construction. This paper has been planned to deal with identification of factors affecting equipment selection, developing a framework for assessing the factors affecting equipment selection. A framework has been developed, which will be used for the future research in this area.

Mearg Ngusse

cuong pham phu

Mechanization and automation constitute an essential stage in the production and operation of any company, as one of the determinants of increase in labor productivity and decrease in product price, while significantly contributing to shortening of the lead time. Businesses are, therefore, able to quickly put projects into operation, improving economic efficiency, quality, and aesthetics, which speeds up the national economic growth. For the construction industry to be the most effective, modern construction equipment is a necessity. It is one of the five main resources of a construction project. Thus, effective construction equipment management contributes to the success of a project and benefits the relevant construction companies economically. This paper presents the critical risk factors affecting equipment management and proposes suitable solutions. The questionnaire-based survey with experienced experts in the construction sector on the management of the likelihood and consequ...

IRJET Journal

Construction equipment are the major resources in infrastructure projects. Construction equipment occupy major portion of project cost, but improper utilization of such resources lead to loss of productivity, ultimately affecting profit. Thus, there is need to optimize these operations to reduce the cost. The present research attempt to design the optimized equipment fleet by studying existing fleet and using optimization for the highway project. It includes an economic analysis of equipment fleet with respect to time and cost. To perform these optimization, production capacity and cost of equipment is taken into consideration. To validate the results, case study of four laning of Sangli-Solapur Section of NH 166 is taken.

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Optimizing equipment selection in heavy earthwork operations is a critical key in the success of any construction project. The objective of this research incentive was geared towards developing a computer model to assist contractors and construction managers in estimating the cost of heavy earthwork operations. Economical operation analysis was conducted for an equipment fleet taking into consideration the owning and operating costs involved in earthwork operations. The model is being developed in a Microsoft environment and is capable of being integrated with other estimating and optimization models. In this study, Caterpillar® Performance Handbook [5] was the main resource used to obtain specifications of selected equipment. The implementation of the model shall give optimum selection of equipment fleet not only based on cost effectiveness but also in terms of versatility. To validate the model, a case study of an actual dam construction project was selected to quantify its degree...

International Journal of Engineering Research & Technology (IJERT)

IJERT Journal

https://www.ijert.org/study-on-factors-affecting-equipment-management-and-its-effect-on-productivity-in-building-construction https://www.ijert.org/research/study-on-factors-affecting-equipment-management-and-its-effect-on-productivity-in-building-construction-IJERTV9IS040176.pdf Construction equipment is one of the important resources of modern day construction in the building sites. Equipment selection is an important factors in the implementation of many construction projects. Equipment managers are habitually called upon to make economical decisions involving the machines in their charge. Their duties includes repairs, rebuilds, and maintenance. Around 30-40% of construction cost overrun is caused by inappropriate supervision of equipments. The purpose of this paper is to identify the various factors affecting equipment management which effects the productivity in construction sites. The main goal of the research study is to provide essential information about factors affecting equipment management to the project management teams who enable the project's success. The questionnaire was categorized into the profile of respondents and the factors in three different groups. This profile were created to collect the information such as job position, experience and contact information. Factors affecting equipment productivity were ranked on the basis of relative importance index method. Based on analysis, the factors which affect equipment productivity are late inspection, condition of sites, operators efficiency and availability of skilled operators.

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CASE STUDIES OF EFFECT OF ROADS ON DEVELOPMENT

A SERIES OF CASE STUDIES WAS CONDUCTED IN LATIN AMERICA TO INDICATE THE IMPACTS OF ROAD CONSTRUCTION ON ECONOMIC DEVELOPMENT. THE COUNTRIES INCLUDED IN THE CASE STUDY WORK WERE EL SALVADOR, GUATEMALA, NICARAGUA, VENEZUELA, AND BOLIVIA. IN ADDITION, ANALYSIS WAS MADE OF A NUMBER OF OTHER CASES PREVIOUSLY STUDIED BY OTHER ORGANIZATIONS IN INDIA, THAILAND, NORTH BORNEO, UGANDA, AND PERU. IN EVERY CASE THE EXTENT OF NEW TRAFFIC GENERATED DEPENDED MAINLY ON THE AVAILABILITY OF EASILY EXPLOITABLE NATURAL RESOURCES. THE LOWEST LEVELS OF TRAFFIC, OMITTING THE EARTH ROADS IN UGANDA AND NORTH BORNEO FOR WHICH TRAFFIC ESTIMATES ARE LACKING, WERE ASSOCIATED WITH HIGHWAYS TRAVERSING A TERRITORY POOR IN RESOURCES. THE HIGHEST TRAFFIC VOLUMES WERE FAIRLY CONSISTENTLY ASSOCIATED WITH RISING NET RECEIPTS TO PRODUCERS OF AGRICULTURAL PRODUCTS. NO ADEQUATE DETAILS OF ACTUAL OR POSSIBLE PROFIT PROSPECTS WERE PROVIDED, BUT THE INFERENCE IS CLEAR THAT THEY CREATED A POWERFUL INDUCEMENT TO RAISE OUTPUT AND SELL A GREATER PROPORTION OF IT IN LOCAL, SECTIONAL, OR WORLD MARKETS. EXAMPLES ARE GIVEN IN GUATEMALA, BOLIVIA AND INDIA WHERE THE HIGHWAY IS BEST CONSTRUED AS A RESPONSE TO A DEVELOPMENT THAT WOULD PROBABLY HAVE OCCURRED IN ANY EVENT, ALTHOUGH NOT NECESSARILY IN THE SAME MANNER NOR TO THE SAME EXTENT. THE EXAMPLES SUGGEST THAT, GIVEN A RELATIVELY STATIC OR DETERIORATING SITUATION BEFORE THE NEW TRANSPORTATION CAPACITY, ALONG WITH FEW READILY EXPLOITABLE NATURAL RESOURCES, THE TASK OF INITIATING SUSTAINABLE GROWTH IS BOTH MORE DIFFICULT AND PROTRACTED, WHICH IMPLIES THE NECESSITY OF COMBINING TRANSPORT INVESTMENT WITH OTHER POLICIES IF IMPORTANT CHANGES ARE TO OCCUR. THE THREE COUNTRIES WITH THE SLOWEST RATE OF OVERALL GROWTH COINCIDE WITH THESE RELATIVELY UNSUCCESSFUL CASES. THIS SUGGESTS THAT WHERE THERE IS A GENERAL LACK OF DYNAMISM THERE IS ALSO A GREATER PROBABILITY THAT A SPECIFIC INVESTMENT WILL NOT BECOME MUCH OF A SUCCESS.

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• TRT Terms: Agriculture ; Economic development ; Field studies ; Highways ; Improvements ; Natural resources ; Road construction ; Traffic volume ; Transportation ; Transportation planning ; Trip generation
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International Journal of Construction Engineering and Management

p-ISSN: 2326-1080    e-ISSN: 2326-1102

2021;  10(4): 101-115

doi:10.5923/j.ijcem.20211004.02

Received: Apr. 27, 2021; Accepted: Sep. 8, 2021; Published: Sep. 26, 2021

Assessment of the Factors Influencing Performance of Road Construction Projects in Uganda: A Case Study of Ministry of Works and Transport

S. Seninde , L. Muhwezi, J. Acai

Department of Civil and Environmental Engineering, Faculty of Engineering, Kyambogo University, Kampala, Uganda

Copyright © 2021 The Author(s). Published by Scientific & Academic Publishing.

The road construction projects in Uganda suffer from many problems and complex issues in performance such as; cost, time, scope and quality. The aim of this study was to assess the factors influencing performance of road construction projects in Uganda. The study adopted a descriptive research design and data were collected using questionnaires from 147 purposively selected respondents from construction companies, consultancy firms, and government. Relevant literature was reviewed to establish actual factors influencing performance of road construction projects in Uganda. Data were coded and entered into statistical packages for social scientists (SPSS) version 25. Data were analyzed descriptively using statistical correlation and regression analysis, and Relative Importance Index (RII) was used to rank the identified factors. The research study revealed five most significant and influential factors on the performance of road construction projects in Uganda and these included; contractors, clients/owners, contractor’s ability to mobilize to site, availability of funds/ cash flows, and site instructions and quality control; and three least influential factors; inadequate mobilization of resources, inadequate geotechnical and hydrological studies, and lack of equipment. The study concluded that contract management, Project stake holders’ factors and project financing factors significantly influence the performance of road construction projects in Uganda.

Keywords: Assessment, Performance, Road construction projects, Relative importance index

Cite this paper: S. Seninde, L. Muhwezi, J. Acai, Assessment of the Factors Influencing Performance of Road Construction Projects in Uganda: A Case Study of Ministry of Works and Transport, International Journal of Construction Engineering and Management , Vol. 10 No. 4, 2021, pp. 101-115. doi: 10.5923/j.ijcem.20211004.02.

Article Outline

1. introduction, 2. literature review, 2.1. key concepts of project performance, 2.2. problem of performance in construction industry, 2.3. construction management and performance, 2.4. information technology and construction projects performance, 2.5. factors affecting performance of managers, 2.6. factors affecting cost and time performance, 2.7. factors affecting road construction performance, 3. research methodology, 3.1. research design and approach/strategy, 3.2. study population, 3.3. sample size, 3.4. area of study, 3.5. data collection methods, 3.6. data collection instruments, 3.7. validity of the instruments, 3.8. reliability of the instrument, 3.9. data analysis, 3.10. attainment of research objectives, 3.10.1. establishing the factors influencing the performance of road construction projects, 3.10.2. establishing the extent of impact of the factors on performance of road construction projects, 3.10.3. establishing the relationship between the factors influencing performance and the performance of road construction projects, 3.10.4. developing a frame work to improve performance of road construction projects, 4. findings and discussions, 4.1. demographics of the respondents, 4.2. empirical findings, 4.2.1. establishing factors influencing the performance of road construction projects in uganda, 4.2.2. ranking of the main factors influencing the performance of road construction projects, 4.2.3. extent of impact of the main factors influencing performance of road construction projects, 4.2.4. extent of impact of the sub-factors of all the main factors influencing the performance of road construction projects, 4.2.5. most influential and impacting factors on performance of road construction projects in uganda, 4.2.6. correlation analysis of the main factors influencing performance of road construction projects in uganda, 4.2.7. a framework to improve the performance of road construction projects in uganda, 5. conclusions and recommendations, 5.1. conclusions, 5.2. recommendations, 6. limitations of the research, acknowledgements.

International Conference on Computational Science and Its Applications

ICCSA 2020: Computational Science and Its Applications – ICCSA 2020 pp 634–648 Cite as

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

• Vincenzo Barrile 19 ,
• Antonino Fotia 19 ,
• Ernesto Bernardo 19 ,
• Giuliana Bilotta 20 &
• Antonino Modafferi 19  
• Conference paper
• First Online: 29 September 2020

1435 Accesses

12 Citations

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12252))

Road infrastructures systems are critical in many regions of Italy, counting thousands of bridges and viaducts that were built over several decades. A monitoring system is therefore necessary to monitor the health of these bridges and to indicate whether they need maintenance.

Different parameters affect the health of an infrastructure, but it would be very difficult to install a network of sensors of various kinds on each viaduct.

For this purpose, we want to finalize the use of geomatics technologies to monitor infrastructures for early warning issues and introducing automations in the data acquisition and processing phases.

This study describes an experimental sensor network system, based on long term monitoring in real-time while an adaptive neuro-fuzzy system is used to predict the deformations of GPS-bridge monitoring points.

The proposed system integrates different data (used to describe the various behaviour scenarios on the structural model), and then it reworks them through machine learning techniques, in order to train the network so that, once only the monitored parameters (displacements) have been entered as input data, it can return an alert parameter.

So, the purpose is to develop a real-time risk predictive system that can replicate various scenarios and capable to alert, in case of imminent hazards. The experimentation conducted in relation to the possibility of transmitting an alert parameter in real time (transmitted through the help of an experimental control unit) obtained by predicting the behavior of the structure using only displacement data during monitoring is particularly interesting.

• Road infrastructures

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Barrile, V., Fotia, A., Bernardo, E., Bilotta, G., Modafferi, A. (2020). Road Infrastructure Monitoring: An Experimental Geomatic Integrated System. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_46

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