Microsoft Word - ETASR_V13_N3_pp10622-10629 Engineering, Technology & Applied Science Research Vol. 13, No. 3, 2023, 10622-10629 10622 www.etasr.com Nguyen et al.: An Application of Analytic Network Process (ANP) to Assess Critical Risks of Bridge … An Application of Analytic Network Process (ANP) to Assess Critical Risks of Bridge Projects in the Mekong Delta Region Minh Duc Nguyen Faculty of Transport Economics, Ho Chi Minh City University of Transport, Vietnam duc.nguyen@ut.edu.vn (corresponding author) Phu Quang Tran Faculty of Transport Economics, Ho Chi Minh City University of Transport, Vietnam phu.tran@ut.edu.vn Hoang Ba Nguyen Faculty of Transportation Engineering, Ho Chi Minh City University of Transport, Vietnam hoangnb@hcmutrans.edu.vn Received: 21 February 2023 | Revised: 9 March 2023 | Accepted: 17 March 2023 Licensed under a CC-BY 4.0 license | Copyright (c) by the authors | DOI: https://doi.org/10.48084/etasr.5802 ABSTRACT Risk management is one of the critical factors contributing to infrastructure project success. Risk assessment enables both practitioners and decision-makers to identify and analyze potential risks and quantify risk impacts on project performance in terms of time, cost, and quality. Even though many studies attempt to investigate the risk of construction projects with the consideration of technical, organizational, and legal aspects, only a few studies deeply focus on identifying the critical risks of bridge projects with the examination of climate change impact. The current study concentrates on analyzing risks in bridge construction projects in the Mekong Delta region which has been significantly affected by climate change. An intensive review of previous publications and technical project reports from 2010 to 2021 was conducted to identify the list of potential risks and interviews and discussions with engineers and managers involved in bridge projects were carried out to identify critical risks of bridge projects. Analytic Network Process (ANP) method was introduced to evaluate the impact of such risks on the performance of bridge project implementation. The initial results of this study provide a holistic picture of risk management for bridge projects with the consideration of climate change impact. The findings can help the involved parties including owners, contractors, and project managers to assess particular risks and scheme backup plans to mitigate project delays and cost overruns. Keywords-risk assessment; risk management; bridge projects; Mekong Delta I. INTRODUCTION Risk is defined as a positive or negative deviation of a variable from its expected value and is commonly understood as a loss [1]. Due to potential risks, bridge construction projects often fail to achieve their time, quality, budget and operation goals [2]. Risk management plays a vital role in minimizing the negative consequences of unexpected events on the bridge project performance. However, risk management for bridge construction projects is a challenging process with the involvement of uncertainty factors from policy, society, economy, environment, finance, and technology [3]. Even though many studies have attempted to investigate the separate risks of bridge construction projects, only a few studies provide a holistic picture of risk management regarding bridge construction projects. Particularly very few scholars focus on assessing the potential risks of bridge projects in the context of developing countries. Thus, a case study in Vietnam with the consideration of bridge projects in the Mekong Delta region is carefully examined in this paper. The selected case study, the Mekong Delta region, encompasses a large portion of southwestern Vietnam, with an area over 40,500km 2 , which has a large number of ongoing bridge projects [4]. Bridge construction projects in this area play a vital role in connecting the Southern areas of Vietnam, and improving the agricultural trading capacity which directly contributes to the local economic growth [5]. Risk management for bridge projects in the Mekong Delta is considered monumental in ensuring the success of strategic connection goals of local governments. In practice, conventional studies on risk assessment in technical and organizational terms have been carried out, but there is a Engineering, Technology & Applied Science Research Vol. 13, No. 3, 2023, 10622-10629 10623 www.etasr.com Nguyen et al.: An Application of Analytic Network Process (ANP) to Assess Critical Risks of Bridge … lack of studies concentrating on potential risks caused by climate change. Therefore, in this paper, we aim to provide a holistic picture of risk management by considering potential risks caused by climate change. The Analytic Network Process (ANP) is a promising approach [6] that was selected to identify critical risk factors and assess the impacts of risk on project objectives including time, cost, quality, and operation. II. RISK IDENTIFICATION A. Literature Review In this study, a mini scoping technique was applied for searching articles. The SCOPUS database was selected for searching with the use of the combination of keywords: Construction Projects AND Bridge projects AND Risk management OR Risk identification OR Risk assessment within a specific period from 2000 to 2020. The initial search resulted in 192 articles related to construction project risk management. Next, we focused on the risks of bridge projects to eliminate some articles and a shortlist of 52 articles was produced. Further, our research team carefully read the titles and abstracts and skimmed the whole content of the studies and finally, 28 of 52 publications were selected for analysis. B. Documentation Review The Mekong Delta region in Vietnam is one of the affected places by climate change [1]. The implementation of bridge projects in this region suffers from many difficulties and potentially contains significant risks in comparison with bridge construction conditions in other regions in Vietnam. In this study, we carefully examined the documentation of real bridge projects in the Mekong Delta region during the period from 2000 to 2020. As a result, 30 typical projects were selected to clarify practical risks which were used to compare to risks identified from the literature. C. Risk Identification Results Through the survey of literature review along with the project documentation review, a list of critical risk factors was identified and is presented in Table I. These risk factors were classified into the following groups: Political and legal risks, Social risks, Technical risks, Economic risks, Environmental risks, and Financial and Commercial risks. III. APPLICATION OF ANALYTIC NETWORK PROCESS (ANP) TO ASSESS CRITICAL RISKS A. Analytic Network Process Model ANP is widely used for multi-criteria-decision-making [52]. ANP is a multi-criteria theory of measurement used to derive relative priority scales of absolute numbers from individual judgments that also belong to the fundamental scale of absolute numbers [53]. The judgments in the ANP model show the relative impact of one of two elements over the other in a pairwise comparison process on a third element in the system [53]. In ANP, pairwise comparisons of the elements in each level are conducted with respect to their relative importance towards their control criterion. TABLE I. RISK FACTORS IN BRIDGE CONSTRUCTION PROJECTS IN THE MEKONG DELTA REGION No Code Risks References I PL Political and legal risks PL1 Changes in the state organization [2-11] PL2 Political interference [2-5, 12, 13] PL3 Legal procedures are complicated and unclear [3, 9, 14-18] PL4 Changes in legal documents and regulations [3, 8, 9, 16-18] PL5 Corruption of officials [3, 17, 19, 20] II SO Social risks SO1 Difficulty in ground clearance [6, 9, 15, 21-25] SO2 Compensation cost [23-25] SO3 Opposition of the community [6, 9, 15, 21, 22] SO4 Disputes in projects [26-29] SO5 Security at the construction site [26, 28, 29] SO6 Local resident consensus [19, 30-33] III TE Technical risks TE1 Lack of management method [29, 23] TE2 Errors in defining project scope [4, 7, 14, 29, 35] TE3 Incomplete and inaccurate survey and experimental data [4, 16, 36, 37] TE4 Inappropriate project approaches [9, 10, 21, 37-39] TE5 Errors in design appraisal and estimation [13, 40] TE6 Non-compliance with construction standards [4, 7, 14, 29, 41] TE7 Changing design and technical plans [4, 17, 18] TE8 Supply delays [10, 37-39] TE9 Unsuitable construction organization [8, 14, 21, 37] TE10 Machinery breakdown [8, 14, 17, 22, 36, 39] TE11 Capacity of contractors, supervision consultants [8, 10, 15, 37, 38, 42, 43] IV EC Economic risks EC1 Inflation [3, 36] EC2 Interest rates change [3, 4, 43, 44] EC3 Economic policy [3, 4, 9, 10, 37, 44] EC4 Currency exchange rate changes [3, 6, 22, 36] EC5 Economic depression [3, 4, 9, 10, 37, 43, 44] EC6 Fuel/material prices change [13, 45] EC7 Macroeconomics [3, 4, 9, 10, 37, 43, 44] EC8 The market changes [21, 39, 46] V EN Environment risks EN1 Unstable geology [6, 9, 15, 21, 22, 47-49] EN2 Sea level rise [10, 14, 21, 43, 50] EN3 Erosion [10, 14, 21, 50] EN4 Changes in rainy season, rainfall [2, 7, 10, 14, 21, 43, 50] EN5 Temperature change [2, 7, 10, 14, 21, 43, 50] EN6 Saltwater intrusion EN7 Storms [6, 8, 50] EN8 Floods [6, 8, 50] VI FC Financial and Commercial risks FC1 Project funding [6, 22, 51] FC2 The ability to attract finance for the project [6, 22, 51] FC3 Financial capacity of the contractor [8, 14, 22] FC4 Weak financial management [4, 16, 21, 46] FC5 Disrupted commercial activities [14, 20, 24, 25, 30] FC6 Compensation for accidents [2, 22] FC7 Weak contract management [10, 14, 15, 21, 22] FC8 Weak cost management [5, 18, 21, 50] FC9 Exceeded estimated cost [5, 17, 18, 21, 20] Engineering, Technology & Applied Science Research Vol. 13, No. 3, 2023, 10622-10629 10624 www.etasr.com Nguyen et al.: An Application of Analytic Network Process (ANP) to Assess Critical Risks of Bridge … Once the pairwise comparisons are completed for the whole network, the vectors corresponding to the maximum eigenvalues of the constructed matrices are computed and a priority vector is obtained. The outcome of the comparison process is used in the development of the unweighted matrix, weighted matrix, and limit matrix. The super matrices’ outcome of ANP is used to assess the priority of risk groups as in Figure 1. The goal of the ANP model in this study is to assess the priority of risks with the consideration of successful criteria of a bridge project. In the literature, there are common perspectives on success criteria for a construction project, as is clearly shown in Table II. Fig. 1. The ANP model assesses the priority of risks. TABLE II. SUMMARY OF SUCCESS CRITERIA No Success criteria Reference 1 Cost, Time, Performance, Satisfaction, Operation, Effectiveness [54] 2 Cost, Time, Technical Requirements, Customer Satisfaction, Operation [55] 3 Time Performance, Cost Performance, Quality Performance, Health, Safety and Environment (HSE), Client satisfaction [56] 4 Cost, Time, Meeting the technical specifications, Customer satisfaction, Stakeholder satisfaction, Operation [57] 5 Cost, Time, Quality, Scope, Customer satisfaction, Safety, Team satisfaction, Shareholder satisfaction [58] Conventionally, successful criteria for a construction project often include time, cost, and quality. However, the efficiency of operating bridge projects is a major concern. Thus, we decided to select 4 main successful criteria, i.e. quality, cost, time, and operation to assess the priority of risks to ensure comprehensive risk assessment. In addition, the alternatives in the ANP model are 6 main risk groups (Figure 1) consisting of Political and Legal risks (PL), Social risks (SO), Technical risks (TE), Economic risks (EC), Environmental risks (EN), and Financial and Commercial risks (FC). B. Questionnaire Survey The identification of risk factors obtained in the literature review and documentation review (Table I) was used to design a questionnaire survey. The survey was then carried out to collect data from project stakeholders including project owners, project managers, contractors, sub-contractors and consultants who have been involved in the bridge construction projects in the Mekong Delta region. The respondents were asked to rate the impact level of critical risks on project cost, time, quality, and operation on the scale shown in Table III. TABLE III. SCORE OF IMPORTANCE Score Importance Score Importance Score Importance 1 Extremely low importance 4 Moderately low importance 7 High importance 2 Very low importance 5 Moderate importance 8 Very high importance 3 Low importance 6 Moderately high importance 9 Extremely high importance IV. RESEARCH RESULTS A. Questionnare Participants A total of 170 survey questionnaires were sent to experts in the bridge construction field. Face-to-face and online modes were used to survey. We received 88 full responses, accounting for around 53% of the total number of target participants. Most respondents have more than 5-year experience, and particularly more than 32% of respondents have more than 10-year experience in the construction industry domain. Fig. 2. Stakeholder percentages Fig. 3. Expert experience percentages. Engineering, Technology & Applied Science Research Vol. 13, No. 3, 2023, 10622-10629 10625 www.etasr.com Nguyen et al.: An Application of Analytic Network Process (ANP) to Assess Critical Risks of Bridge … B. Impact Risk Level Rating by MSI (Mean Score of Importance) Results Six main groups and 47 associated risk factors were assessed by experts through the questionnaire survey. The experts participating in the survey were asked to rate the importance of the risk prioritization with the consideration of four main project objectives: Quality cost, time, and operation. The final results are shown in Tables IV-XI. TABLE IV. IMPORTANCE OF THE CRITERIA THROUGH MSI Criteria Min score Max score Average score Ranking Importance of quality 7 9 8.44 1 Importance of cost 6 9 7.64 3 Importance of time 6 9 7.57 4 Importance of operation 7 9 8.07 2 TABLE V. IMPORTANCE OF RISK GROUPS Risk groups Importance Average score Ranking Quality Cost Time Operation PL 4.78 5.94 6.78 6.38 5.97 5 SO 4.17 5.50 6.50 6.22 5.59 6 TE 7.28 6.83 7.16 7.11 7.09 1 EC 5.84 6.78 6.17 6.61 6.35 3 EN 6.00 6.33 6.22 7.22 6.44 2 FC 5.72 6.83 6.17 6.11 6.20 4 TABLE VI. IMPORTANCE OF RISK FACTORS IN POLITICAL AND LEGAL GROUPS Risk Factors The importance for Average score Ranking Quality Cost Time Operation PL1 5.11 5.44 5.33 4.72 5.15 5 PL2 5.28 5.72 5.33 5.44 5.44 4 PL3 5.78 7.17 7.00 6.33 6.57 1 PL4 6.06 6.94 6.78 6.27 6.51 2 PL5 6.11 6.78 5.94 5.77 6.15 3 TABLE VII. IMPORTANCE OF RISK FACTORS IN SOCIAL GROUP Risk Factors The importance for Average score Ranking Quality Cost Time Operation SO1 6.00 7.89 7.83 6.77 7.12 1 SO2 5.67 7.50 7.39 6.77 6.83 2 SO3 5.39 6.28 6.83 6.22 6.18 3 SO4 4.94 5.89 6.94 6.05 5.96 5 SO5 4.83 5.56 5.78 5.83 5.50 6 SO6 4.94 6.83 6.39 6.50 6.17 4 TABLE VIII. IMPORTANCE OF RISK FACTORS IN TECHNICAL GROUP Risk Factors The importance for Average score Ranking Quality Cost Time Operation TE1 5.67 6.22 6.00 7.00 6.22 8 TE2 7.39 6.50 6.67 7.11 6.92 5 TE3 6.89 6.33 6.33 7.05 6.65 7 TE4 7.56 6.39 6.72 7.27 6.99 4 TE5 7.78 6.56 6.56 7.27 7.04 3 TE6 6.83 6.28 6.89 6.72 6.68 6 TE7 6.00 6.11 6.28 5.77 6.04 9 TE8 7.22 6.39 6.50 6.50 6.65 7 TE9 6.39 5.67 5.72 5.94 5.93 10 TE10 7.50 7.44 7.33 7.00 7.32 1 TE11 7.17 7.17 7.11 6.94 7.10 2 TABLE IX. IMPORTANCE OF RISK FACTORS IN ECONOMIC GROUP Risk Factors The importance for Average score Ranking Quality Cost Time Operation EC1 5.11 6.39 5.89 5.66 5.76 2 EC2 4.89 6.11 5.89 5.61 5.63 4 EC3 4.89 5.83 6.11 5.66 5.62 5 EC4 4.83 5.56 5.44 5.50 5.33 8 EC5 5.06 6.11 5.61 5.50 5.57 6 EC6 6.11 7.22 6.89 5.72 6.49 1 EC7 5.00 6.22 5.83 5.83 5.72 3 EC8 4.78 6.11 5.56 5.72 5.54 7 TABLE X. IMPORTANCE OF RISK FACTORS IN ENVIRONMENTAL GROUP Risk Factors The importance for Average score Ranking Quality Cost Time Operation EN1 6.94 6.72 6.17 6.27 6.53 1 EN2 4.94 6.11 5.33 4.94 5.33 2 EN3 4.94 6.17 5.44 4.77 5.33 2 EN4 5.22 5.67 5.44 4.77 5.28 3 EN5 4.83 5.06 4.83 4.61 4.83 7 EN6 4.89 5.33 4.94 4.61 4.94 5 EN7 5.00 5.00 4.83 4.72 4.89 6 EN8 5.17 5.22 5.39 4.72 5.13 4 TABLE XI. IMPORTANCE OF RISK FACTORS IN FINANCIAL AND COMMERCIAL GROUPS Risk Factors The importance for Average score Ranking Quality Cost Time Operation FC1 5.83 7.28 7.00 6.22 6.58 1 FC2 5.28 6.78 6.39 5.61 6.02 7 FC3 6.22 7.33 7.00 5.66 6.55 2 FC4 6.00 6.78 6.61 5.77 6.29 4 FC5 5.22 5.28 5.67 5.33 5.38 9 FC6 5.22 5.78 5.56 5.22 5.45 8 FC7 6.17 6.78 6.22 6.05 6.31 3 FC8 6.00 6.44 6.06 6.00 6.13 5 FC9 5.61 6.50 6.28 5.83 6.06 6 The presented survey results helped us to assess the importance level of the risks on specific groups. However, this approach does not accurately reflect the interaction of risks within the whole project. In order to clarify the impact among criteria and risks, the ANP model was applied to overcome obstacles. C. Analytic Network Process Rating of the Impact Level of Risks The purpose of applying the ANP model is to evaluate the priority of the risk groups and risk factors through the calculation of the Risk Priority Index (RPI). This is a method of ranking all risks while taking into account their importance to all considered criteria. Based on the survey results of experts, the MSI has been determined. In addition, along with the opinions of experts in the group discussion, a pairwise comparison matrix for each model will be established which is scored by a pairwise table [53]. 1) Criteria Priority To assess risk priority considering the main criteria, a sub model was established and is depicted in Figure 4. Engineering, Technology & Applied Science Research Vol. 13, No. 3, 2023, 10622-10629 10626 www.etasr.com Nguyen et al.: An Application of Analytic Network Process (ANP) to Assess Critical Risks of Bridge … TABLE XII. PAIRWISE TABLE Level of importance Pairwise comparison score 1: Equally important 1:1 2: Equally important to moderate 2:1, 3:2, 4:3, 5:4, 6:5, 7:6, 8:7, 9:8 3: Moderately important 3:1, 4:2, 5:3, 6:4, 7:5, 8:6, 9:7 4: Moderately important to slightly more important 4:1, 5:2, 6:3, 7:4, 8:5, 9:6 5: Slightly more important 5:1, 6:2, 7:3, 8:4, 9:5 6: Slightly important to very important 6:1, 7:2, 8:3, 9:4 7: Very important 7:1, 8:2, 9:3 8: Very important to extremely important 8:1, 9:2 9: Extremely important 9:1 Fig. 4. Sub model for assessing the priority of criteria. Through the use of Super Decision software, the unweighted super matrix, the weighted super matrix, and the limit matrix of bridge project criteria were calculated and are presented in Tables XIII-XV. TABLE XIII. UNWEIGHTED SUPER MATRIX CONSIDERING THE FOUR MAIN CRITERIA Cluster Quality Cost Time Operation Priority Value 0.400 0.00 0.259 0.310 0.167 0.200 0.310 0.412 0.000 0.333 0.000 0.195 0.327 0.493 0.333 0.400 0.493 0.00 0.195 0.167 TABLE XIV. WEIGHTED SUPER MATRIX CONSIDERING THE FOUR MAIN CRITERIA Cluster Quality Cost Time Operation Priority Value 0.400 0.00 0.259 0.310 0.167 0.200 0.310 0.412 0.000 0.333 0.000 0.195 0.327 0.493 0.333 0.400 0.493 0.00 0.195 0.167 TABLE XV. LIMIT MATRIX CONSIDERING THE FOUR MAIN CRITERIA Cluster Quality Cost Time Operation Priority Value 0.243 0.243 0.243 0.243 0.243 0.236 0.236 0.236 0.236 0.236 0.252 0.252 0.252 0.252 0.252 0.267 0.267 0.267 0.267 0.267 The Normalized Priority Value (NPV), Total Priority Value (TPV), and Ideal Priority Value (IPV) of criteria were calculated and can be seen in Table XVI. TABLE XVI. CRITERIA PRIORITY Criteria TPV NPV IPV Ranking (R) Quality 0.333 0.333 1.000 1 Cost 0.167 0.167 0.500 2 Time 0.167 0.167 0.500 2 Operation 0.333 0.333 1.000 1 2) Risk Group Priority The 6 risk groups of bridge construction projects are Political and legal risks (PL); Social risks (SO); Technical risks (TE); Economic risks (EC); Environmental risks (EN); Financial and Commercial risks (FC). The model in Figure 1 could be used to assess the priority of the risk groups. Through the use of Super Decision software, the unweighted super matrix, the weighted super matrix, and the limit matrix of the bridge project criteria were calculated and are shown in Tables XVII-XIX. TABLE XVII. UNWEIGHTED SUPER MATRIX FOR RISK GROUPS NODES EC EN FC PL SO TE Cost Operation Quality Time A lt e r n a ti v e s EC 0.000 0.206 0.183 0.252 0.219 0.186 0.222 0.166 0.176 0.100 EN 0.201 0.000 0.252 0.100 0.136 0.312 0.111 0.223 0.176 0.200 FC 0.180 0.162 0.000 0.190 0.252 0.121 0.222 0.125 0.176 0.100 PL 0.222 0.182 0.177 0.000 0.205 0.190 0.111 0.152 0.097 0.200 SO 0.171 0.249 0.186 0.271 0.000 0.191 0.111 0.111 0.060 0.200 TE 0.226 0.200 0.201 0.186 0.187 0.000 0.222 0.223 0.315 0.200 C r it e r ia Cost 0.250 0.250 0.250 0.250 0.250 0.250 0.000 0.000 0.000 0.000 Operation 0.250 0.250 0.250 0.250 0.250 0.250 0.000 0.000 0.000 0.000 Quality 0.250 0.250 0.250 0.250 0.250 0.250 0.000 0.000 0.000 0.000 Time 0.250 0.250 0.250 0.250 0.250 0.250 0.000 0.000 0.000 0.000 TABLE XVIII. WEIGHTED SUPER MATRIX FOR RISK GROUPS NODES EC EN FC PL SO TE Cost Operation Quality Time A lt e r n a ti v e s EC 0.000 0.103 0.092 0.126 0.110 0.093 0.222 0.166 0.176 0.100 EN 0.100 0.000 0.126 0.050 0.068 0.156 0.111 0.223 0.176 0.200 FC 0.090 0.081 0.000 0.095 0.126 0.061 0.222 0.125 0.176 0.100 PL 0.111 0.091 0.089 0.000 0.103 0.095 0.111 0.152 0.097 0.200 SO 0.086 0.125 0.093 0.136 0.000 0.095 0.111 0.111 0.060 0.200 TE 0.113 0.100 0.101 0.093 0.094 0.000 0.222 0.223 0.315 0.200 C r it e r ia Cost 0.125 0.125 0.125 0.125 0.125 0.125 0.000 0.000 0.000 0.000 Operation 0.125 0.125 0.125 0.125 0.125 0.125 0.000 0.000 0.000 0.000 Quality 0.125 0.125 0.125 0.125 0.125 0.125 0.000 0.000 0.000 0.000 Time 0.125 0.125 0.125 0.125 0.125 0.125 0.000 0.000 0.000 0.000 Engineering, Technology & Applied Science Research Vol. 13, No. 3, 2023, 10622-10629 10627 www.etasr.com Nguyen et al.: An Application of Analytic Network Process (ANP) to Assess Critical Risks of Bridge … TABLE XIX. LIMIT MATRIX FOR RISK GROUPS NODES EC EN FC PL SO TE Cost Operation Quality Time A lt e r n a ti v e s EC 0.113 0.113 0.113 0.113 0.113 0.113 0.113 0.113 0.113 0.113 EN 0.116 0.116 0.116 0.116 0.116 0.116 0.116 0.116 0.116 0.116 FC 0.102 0.102 0.102 0.102 0.102 0.102 0.102 0.102 0.102 0.102 PL 0.102 0.102 0.102 0.102 0.102 0.102 0.102 0.102 0.102 0.102 SO 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 TE 0.134 0.134 0.134 0.134 0.134 0.134 0.134 0.134 0.134 0.134 C r it e r ia Cost 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 Operation 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 Quality 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 Time 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 0.083 The NPV, TPV, and IPV of risk groups were calculated and are shown in Table XX. TABLE XX. PRIORITY OF RISK GROUPS Risk Group TPV NPV IPV R EC 0.16942 0.16942 0.845723 3 EN 0.174148 0.174148 0.869325 2 FC 0.152897 0.152897 0.763242 4 PL 0.152674 0.152674 0.76213 4 SO 0.150537 0.150537 0.751463 5 TE 0.200325 0.200325 1 1 V. DISCUSSION A. Criteria Priority Through the ANP, this study ranked the risk priority with the consideration of the main assessment criteria of a bridge construction project. Quality and Operation are the most important criteria with a priority value of 0.333 for both. In addition, Cost and Time had equal importance with a score of 0.167. B. Risk Group Priority The Technical risk group is the most critical in a bridge construction project in the Mekong Delta region. This group has significant impacts on the Quality, Cost, Time and Operation of projects. The results are similar with the ones of [9, 36, 37]. The results revealed that technical risk is always the first risk priority for construction project management. The Environmental risk group ranks second among risk groups. This result can be explained by the geographic features of the Mekong Delta region which was significantly influenced by climate change in recent years. The third-ranking group is the Economic risk. This group consists of 8 risk factors, in which "Fuel/material prices change" is assessed as the most important factor. In fact, the main material prices for bridge construction projects have increased dramatically over the years. For instance, the steel price went up around 40% in one year and the cement price jumped up by approximately 90.000VND (3.94$) per ton compared to the price in 2020. Consequently, bridge projects suffered cost overrun, which this is the main cause leading to project delays. Finally, while Political and legal risks and Financial and Commercial risks are ranked equally, the Social risk is considered as having less impact on the bridge construction projects in the Mekong Delta region. This shows that the Vietnam government has provided a sustainable political environment and mainly facilitated consistent policy for construction industry development. The findings of this study are in accordance with the results of recent studies [59-61] and provide insight for policy makers in establishing risk mitigation strategies for large-scale bridge projects. VI. CONCLUSION In this paper, we identified 47 potential risk factors with the focus on bridge construction projects in the Mekong Delta region. Such risk factors have been categorized into 6 main groups for assessment, including Political and legal (PL), Social (SO), Technical (TE), Economic (EC), Environmental (EN), and Financial and Commercial (FC). By reviewing the importance of the risk-given groups with the consideration of 4 main criteria (quality, cost, time, and operation) along with practical surveys to collect expert opinions, the priority of risk groups had been determined. The TE group was ranked at the highest level (IPV = 1) which potentially has the most significant impact on the performance of bridge construction projects. The EN group was ranked at the second-highest level (IPV = 0.8693), which reflects the risk consequences of the climate change on the bridge projects carried out in the Mekong Delta region. In addition, the EC group was also ranked in the top three, which indicates that economic risks have potential influences on the project’s success. The results of this study can enhance project managers to have a backup plan which can mitigate potential risks in the implementation of bridge construction projects in the Mekong delta region. 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