Development of Risk Quantification Models in Road Infrastructure Projects
Abstract
:1. Introduction
Literature Review
2. Methodology and Tools
2.1. Risk Quantification
- Multi-Criteria Decision-Making (MCDM)
- Cluster Analysis
2.2. Software and Tools Used
3. Results
3.1. Project Dataset
3.2. Identification of Factors
3.3. Risk Occurrence in Projects
- Ai—the average number of occurrences of risk i;
- Σfi—the sum of occurrences for each project;
- N—the total number of projects.
3.4. Quantification of ICP and EoT
- EoTc—total number of days of delay per Claim or/and Variation Order;
- EoTRmri—the major risk;
- EoTRsri—the secondary risks;
- n—the number of secondary risks;
- i—the index of the secondary risk, starting from 1.
- ICPc—total number of days of delay per Claim or/and Variation Order;
- ICPRmri—the major risk; ICPRsri—the secondary risks;
- n—the number of secondary risks;
- i—the index of the secondary risk, starting from 1.
3.5. Risk Classification
- EoTmajornorm—the major normalized EoT risk; EoTsecnorm—the secondary normalized EoT risk;
- ICP majornorm—the major normalized ICP risk; ICP secnorm—the secondary normalized ICP risk;
- PoR majornorm—the major normalized PoR; PoR secnorm—the secondary normalized PoR.
- 1.1 Non-compliance of the project with environmental conditions due to inappropriate design bases (0.39);
- 1.11 Inapplicable project documentation for high cuts, including tunnel portals (0.31);
- 1.9 Unresolved collisions with existing infrastructure facilities (underground installations, pipelines, local roads, railways, etc.) (0.28);
- 1.5 Delays in the creation or changes in project documentation during execution (0.26);
- 1.4 Non-compliance in parts of project documentation (0.26).
3.6. Model Validation
4. Discussion
Prioritization of Risks and Preventive Measures
- Risk:
- 1.1 Non-compliance of the project with environmental conditions due to inappropriate design bases.
- Preventive Measures:
- A higher or satisfactory level of geological tests to enable the most accurate preparation of the base (as few approximations as possible);
- Installation of an adequate number of piezometers to monitor the level of underground water;
- Adequate geodetic surveying to create a geodetic base (instead of relying on data from the existing inaccurate network to reduce the costs of geodetic surveying);
- Addressing all holders of public authority for them to issue location conditions;
- Detailed previous archeological research;
- Data on hydrological and climatic impacts must be regularly updated.
- Risk:
- 1.11 Inapplicable project documentation for high cuts, including tunnel portals.
- Preventive Measures:
- A higher or satisfactory level of geological research;
- Conducting basic preliminary field research in the design phase;
- An adequate number of laboratory tests during the development of project documentation;
- Greater control when creating project documentation;
- Agreed penalties for errors in projects;
- Stricter criteria in the designer selection process (requiring more references);
- Holding responsible designers accountable;
- Installation of an adequate number of piezometers to monitor the level of underground water;
- Adequate geodetic surveying to create a geodetic base (instead of relying on data from the existing inaccurate network to reduce the costs of geodetic surveying);
- Introduction of design supervision in practice (during project implementation).
- Risk:
- 1.9 Unresolved collisions with existing infrastructure facilities (underground installations, pipelines, local roads, railways, etc.).
- Preventive Measures:
- Addressing all holders of public authority, including all cable operators and local public companies, for the issuance of location conditions;
- Consistent compliance with the issued location conditions in terms of crossing with and relocation of existing infrastructure facilities;
- Identification of all collisions on the ground before the start of construction in order to determine those that are not included in the location conditions;
- Timely conclusion of contracts on relocation of installations to define mutual relations with their owners;
- Introduction of user supervision of installation owners during the execution of works;
- A contract-defined mechanism for the relocation of uncharted simple communal installations (e.g., rural water lines and sewers);
- Creation of an adequate utility synchronization plan.
- Risk:
- 1.5 Delays in the creation or changes in project documentation during execution.
- Preventive Measures:
- Determination of an adequate deadline for the preparation of project documentation;
- Greater control during the design itself;
- Performing technical control in parallel with the design process (introducing phases);
- More thorough work by members of the Revision Commission (without political influence);
- Agreed penalties for errors in projects;
- Increased responsibility for designers;
- Changes in the local laws in this area to prescribe the designer’s obligation to eliminate all deficiencies in the project documentation;
- Introduction of design supervision in practice (during project implementation).
- Risk:
- 1.4 Non-compliance in parts of project documentation.
- Preventive Measures:
- Determination of an adequate deadline for the preparation of project documentation;
- Greater control during the creation of the basic project;
- Agreed penalties for errors in projects;
- Increased responsibility for designers;
- Changes in the local laws in this area to prescribe the designer’s obligation to eliminate all deficiencies in the project documentation;
- Introduction of design supervision in practice (during project implementation).
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Viswanathan, S.K.; Tripathi, K.K.; Jha, K.N. Influence of Risk Mitigation Measures on International Construction Project Success Criteria–A Survey of Indian Experiences. Constr. Manag. Econ. 2020, 38, 207–222. [Google Scholar] [CrossRef]
- Dandage, R.V.; Mantha, S.S.; Rane, S.B.; Bhoola, V. Analysis of Interactions among Barriers in Project Risk Management. J. Ind. Eng. Int. 2018, 14, 153–169. [Google Scholar] [CrossRef]
- Ahsan, K.; Gunawan, I. Analysis of Cost and Schedule Performance of International Development Projects. Int. J. Proj. Manag. 2010, 28, 68–78. [Google Scholar] [CrossRef]
- Han, S.H.; Kim, D.Y.; Kim, H.; Jang, W.S. A Web-Based Integrated System for International Project Risk Management. Autom. Constr. 2008, 17, 342–356. [Google Scholar] [CrossRef]
- Lee, J.; Jung, D.; Baek, C.; Hu, Y.-C.; Lin, M.-H.; Tsai, J.-F.; Nguyen, P.-H.; Lu, M.-T.; Lee, J.; Jung, D.; et al. An Analytical Study Predicting Future Conditions and Application Strategies of Concrete Bridge Pavement Based on Pavement Management System Database. Sustainability 2023, 15, 16680. [Google Scholar] [CrossRef]
- Deng, X.; Low, S.P. Exploring Critical Variables That Affect Political Risk Level in International Construction Projects: Case Study from Chinese Contractors. J. Prof. Issues Eng. Educ. Pract. 2013, 140, 04013002. [Google Scholar] [CrossRef]
- Wang, S.Q.; Dulaimi, M.F.; Aguria, M.Y. Risk Management Framework for Construction Projects in Developing Countries. Constr. Manag. Econ. 2004, 22, 237–252. [Google Scholar] [CrossRef]
- Chen, D.; Zhang, P.; Pan, T.; Liao, Y.; Zhao, H. Evaluation of the Eco-Friendly Crushed Waste Oyster Shell Mortars Containing Supplementary Cementitious Materials. J. Clean. Prod. 2019, 237, 117811. [Google Scholar] [CrossRef]
- Liao, Y.; Fan, J.; Li, R.; Da, B.; Chen, D.; Zhang, Y. Influence of the Usage of Waste Oyster Shell Powder on Mechanical Properties and Durability of Mortar. Adv. Powder Technol. 2022, 33, 103503. [Google Scholar] [CrossRef]
- Shao, S.; Tian, P.; Zhang, H.; Zhang, H.; Zhang, H.; Li, L. An Analytical Investigation into the Water Film Dynamics at the Connection Lines of Highways and Urban Roadways. J. Urban Dev. Manag. 2023, 2, 181–195. [Google Scholar] [CrossRef]
- Trifunović, A.; Senić, A.; Čičević, S.; Ivanišević, T.; Vukšić, V.; Simović, S. Evaluating the Road Environment through the Lens of Professional Drivers: A Traffic Safety Perspective. Mechatronics Intell. Transp. Syst. 2024, 3, 31–38. [Google Scholar] [CrossRef]
- Ivanišević, T.; Simović, S.; Trifunović, A.; Vukšić, V. Perception of Large Danger Lists and Orange Boards for Marking Transport Units. J. Urban Dev. Manag. 2024, 3, 74–82. [Google Scholar] [CrossRef]
- Stević, Ž.; Subotić, M.; Softić, E.; Božić, B. Multi-Criteria Decision-Making Model for Evaluating Safety of Road Sections. J. Intell. Manag. Decis. 2022, 1, 78–87. [Google Scholar] [CrossRef]
- Kodepogu, K.; Manjeti, V.B.; Siriki, A.B. Machine Learning for Road Accident Severity Prediction. Mechatronics Intell. Transp. Syst. 2023, 2, 211–226. [Google Scholar] [CrossRef]
- Kotapati, G.; Ali, M.A.; Vatambeti, R. Deep Learning-Enhanced Hybrid Fruit Fly Optimization for Intelligent Traffic Control in Smart Urban Communities. Mechatronics Intell. Transp. Syst. 2023, 2, 89–101. [Google Scholar] [CrossRef]
- Kan, H.; Li, K.; Wang, Z. An Integrated Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention Mechanism Model for Enhanced Highway Traffic Flow Prediction. J. Urban Dev. Manag. 2024, 3, 18–33. [Google Scholar] [CrossRef]
- Afzal, F.; Yunfei, S.; Nazir, M.; Bhatti, S.M. A Review of Artificial Intelligence Based Risk Assessment Methods for Capturing Complexity-Risk Interdependencies: Cost Overrun in Construction Projects. Int. J. Manag. Proj. Bus. 2021, 14, 300–328. [Google Scholar] [CrossRef]
- Lozano-Ramírez, N.E.; Sánchez, O.; Carrasco-Beltrán, D.; Vidal-Méndez, S.; Castañeda, K. Digitalization and Sustainability in Linear Projects Trends: A Bibliometric Analysis. Sustainability 2023, 15, 15962. [Google Scholar] [CrossRef]
- Xu, X.; Wang, J.; Li, C.Z.; Huang, W.; Xia, N. Schedule Risk Analysis of Infrastructure Projects: A Hybrid Dynamic Approach. Autom. Constr. 2018, 95, 20–34. [Google Scholar] [CrossRef]
- Assaf, S.A.; Al-Hejji, S. Causes of Delay in Large Construction Projects. Int. J. Proj. Manag. 2006, 24, 349–357. [Google Scholar] [CrossRef]
- Luu, V.T.; Kim, S.Y.; Van Tuan, N.; Ogunlana, S.O. Quantifying Schedule Risk in Construction Projects Using Bayesian Belief Networks. Int. J. Proj. Manag. 2009, 27, 39–50. [Google Scholar] [CrossRef]
- Silva, G.A.; Warnakulasooriya, B.N.F.; Arachchige, B. Criteria for Construction Project Success: A Literature Review. SSRN Electron. J. 2016. [Google Scholar] [CrossRef]
- Sarkar, S.; Bhaskar, M.S.; Rao, K.U.; Prema, V.; Almakhles, D.; Subramaniam, U. Solar PV Network Installation Standards and Cost Estimation Guidelines for Smart Cities. Alex. Eng. J. 2022, 61, 1277–1287. [Google Scholar] [CrossRef]
- Elfahham, Y. Estimation and Prediction of Construction Cost Index Using Neural Networks, Time Series, and Regression. Alex. Eng. J. 2019, 58, 499–506. [Google Scholar] [CrossRef]
- Allahi, F.; Cassettari, L.; Mosca, M. Stochastic Risk Analysis and Cost Contingency Allocation Approach for Construction Projects Applying Monte Carlo Simulation. In Proceedings of the World Congress on Engineering, London, UK, 5–7 July 2017; pp. 385–391. [Google Scholar]
- Vegas-Fernández, F. Project Risk Costs: Estimation Overruns Caused When Using Only Expected Value for Contingency Calculations. J. Manag. Eng. 2022, 38, 04022037. [Google Scholar] [CrossRef]
- Flyvbjerg, B.; Bester, D.W. The Cost-Benefit Fallacy: Why Cost-Benefit Analysis Is Broken and How to Fix It. J. Benefit-Cost Anal. 2021, 12, 395–419. [Google Scholar] [CrossRef]
- Valipour, A.; Yahaya, N.; Md Noor, N.; Kildiene, S.; Sarvari, H.; Mardani, A. A Fuzzy Analytic Network Process Method for Risk Prioritization in Freeway PPP Projects: An Iranian Case Study. J. Civ. Eng. Manag. 2015, 21, 933–947. [Google Scholar] [CrossRef]
- Fang, C.; Marle, F. Dealing with Project Complexity by Matrix-Based Propagation Modelling for Project Risk Analysis. J. Eng. Des. 2013, 24, 239–256. [Google Scholar] [CrossRef]
- Qazi, A.; Quigley, J.; Dickson, A.; Kirytopoulos, K. Project Complexity and Risk Management (ProCRiM): Towards Modelling Project Complexity Driven Risk Paths in Construction Projects. Int. J. Proj. Manag. 2016, 34, 1183–1198. [Google Scholar] [CrossRef]
- Islam, M.S.; Nepal, M.P.; Skitmore, M.; Attarzadeh, M. Current Research Trends and Application Areas of Fuzzy and Hybrid Methods to the Risk Assessment of Construction Projects. Adv. Eng. Inform. 2017, 33, 112–131. [Google Scholar] [CrossRef]
- Islam, M.S.; Nepal, M. A Fuzzy-Bayesian Model for Risk Assessment in Power Plant Projects. Procedia Comput. Sci. 2016, 100, 963–970. [Google Scholar] [CrossRef]
- Hsiao, H.S.; Chen, J.C. Using a Gesture Interactive Game-Based Learning Approach to Improve Preschool Children’s Learning Performance and Motor Skills. Comput. Educ. 2016, 95, 151–162. [Google Scholar] [CrossRef]
- Fang, C.; Marle, F. A Simulation-Based Risk Network Model for Decision Support in Project Risk Management. Decis. Support Syst. 2012, 52, 635–644. [Google Scholar] [CrossRef]
- Liu, J.; Zhao, X.; Yan, P. Risk Paths in International Construction Projects: Case Study from Chinese Contractors. J. Constr. Eng. Manag. 2016, 142, 05016002. [Google Scholar] [CrossRef]
- Pehlivan, S.; Öztemir, A.E. Integrated Risk of Progress-Based Costs and Schedule Delays in Construction Projects. Eng. Manag. J. 2018, 30, 108–116. [Google Scholar] [CrossRef]
- Kululanga, G.; Kuotcha, W. Measuring Project Risk Management Process for Construction Contractors with Statement Indicators Linked to Numerical Scores. Eng. Constr. Archit. Manag. 2010, 17, 336–351. [Google Scholar] [CrossRef]
- Dandage, R.; Mantha, S.S.; Rane, S.B. Ranking the Risk Categories in International Projects Using the TOPSIS Method. Int. J. Manag. Proj. Bus. 2018, 11, 317–331. [Google Scholar] [CrossRef]
- Issa, U.H.; Mosaad, S.A.A.; Salah Hassan, M. Evaluation and Selection of Construction Projects Based on Risk Analysis. Structures 2020, 27, 361–370. [Google Scholar] [CrossRef]
- Khalilzadeh, M.; Banihashemi, S.A.; Božanić, D. A Step-By-Step Hybrid Approach Based on Multi-Criteria Decision-Making Methods and A Bi-Objective Optimization Model to Project Risk Management. Decis. Mak. Appl. Manag. Eng. 2024, 7, 442–472. [Google Scholar] [CrossRef]
- Nikolić, I.; Milutinović, J.; Božanić, D.; Dobrodolac, M. Using an Interval Type-2 Fuzzy AROMAN Decision-Making Method to Improve the Sustainability of the Postal Network in Rural Areas. Mathematics 2023, 11, 3105. [Google Scholar] [CrossRef]
- Drobne, S.; Lisec, A. Multi-Attribute Decision Analysis in GIS: Weighted Linear Combination and Ordered Weighted Averaging. Informatica 2009, 33, 459. [Google Scholar]
- Chan, D.W.M.; Kumaraswamy, M.M. A Comparative Study of Causes of Time Overruns in Hong Kong Construction Projects. Int. J. Proj. Manag. 1997, 15, 55–63. [Google Scholar] [CrossRef]
- Gondia, A.; Siam, A.; El-Dakhakhni, W.; Nassar, A.H. Machine Learning Algorithms for Construction Projects Delay Risk Prediction. J. Constr. Eng. Manag. 2019, 146, 04019085. [Google Scholar] [CrossRef]
- Gunduz, M.; Yahya, A.M.A. Analysis of Project Success Factors in Construction Industry. Technol. Econ. Dev. Econ. 2018, 24, 67–80. [Google Scholar] [CrossRef]
- Rachid, Z.; Toufik, B.; Mohammed, B. Causes of Schedule Delays in Construction Projects in Algeria. Int. J. Constr. Manag. 2019, 19, 371–381. [Google Scholar] [CrossRef]
- Santoso, D.S.; Soeng, S. Analyzing Delays of Road Construction Projects in Cambodia: Causes and Effects. J. Manag. Eng. 2016, 32, 05016020. [Google Scholar] [CrossRef]
- Marle, F.; Vidal, L.A. Project Risk Management Processes: Improving Coordination Using a Clustering Approach. Res. Eng. Des. 2011, 22, 189–206. [Google Scholar] [CrossRef]
- Kv¥lseth, T.O. Cautionary Note about R. Am. Stat. 1985, 39, 279–285. [Google Scholar] [CrossRef]
- Chicco, D.; Warrens, M.J.; Jurman, G. The Coefficient of Determination R-Squared Is More Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation. PeerJ Comput. Sci. 2021, 7, e623. [Google Scholar] [CrossRef]
- Willmott, C.J.; Matsuura, K. Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Clim. Res. 2005, 30, 79–82. [Google Scholar] [CrossRef]
- Chen, L.; Manley, K. Validation of an Instrument to Measure Governance and Performance on Collaborative Infrastructure Projects. J. Constr. Eng. Manag. 2014, 140, 04014006. [Google Scholar] [CrossRef]
- Acampa, G.; Marino, G.; Ticali, D. Validation of Infrastructures through BIM. In AIP Conference Proceedings; AIP Publishing: Melville, NY, USA, 2019; Volume 2186. [Google Scholar]
- Bratu, P.; Tonciu, O.; Nițu, M.C. Modeling the Vibratory Compaction Process for Roads. Buildings 2023, 13, 2837. [Google Scholar] [CrossRef]
- Zakarka, M.; Skuodis, Š.; Dirgėlienė, N. Triaxial Test of Coarse-Grained Soils Reinforced with One Layer of Geogrid. Appl. Sci. 2023, 13, 12480. [Google Scholar] [CrossRef]
Authors | The Title of the Paper | Year | Methodology | Key Research Results |
---|---|---|---|---|
Wang, Dulaimi, and Aguria [7] | Risk management framework for construction projects in developing countries | 2004 | Alien Eyes’ Risk Model | Twenty-eight critical risks were identified and categorized on three (state, market, and project) hierarchical levels, and their criticality was assessed and ranked. |
Luu, Kim, Van, and Ogunlana [21] | Quantifying schedule risk in construction projects using Bayesian belief networks | 2009 | Bayesian belief networks | Sixteen factors were identified that can have a direct impact on exceeding deadlines. |
Kululanga and Kuotcha [37] | Measuring project risk management process for construction contractors, with statement indicators linked to numerical scores | 2010 | Statistical analysis | Most of the variables in the project risk management process were positively and significantly related to the progression in size and the experience of construction contractors. |
Dandage, Mantha, and Rane [38] | Ranking the risk categories in international projects using the TOPSIS method | 2018 | TOPSIS method | Identification of eight different types of risk categories associated with international projects. |
Viswanathan, Tripathi, and Jha [1] | Influence of risk mitigation measures on international construction project success criteria—a survey of Indian experiences | 2020 | Factor analysis | The positive relationship between risk mitigation factors and project success (coefficient 0.8). |
Issa, Mosaad, and Salah Hassan [39] | Evaluation and selection of construction projects based on risk analysis | 2020 | AHP, Fuzzy risk analysis model | Five criteria and seventy factors influencing the decision of the contractor were identified, and the weight and importance of each criterion were determined. |
Khalilzadeh, Banihashemi, and Božanić [40] | A Step-By-Step Hybrid Approach Based on Multi-Criteria Decision-Making Methods and A Bi-Objective Optimization Model to Project Risk Management | 2024 | Fuzzy method | An innovative and reliable hybrid approach based on MCDM and mathematical optimization methods was proposed. |
Our Study | Development of Risk Quantification Models in Road Infrastructure Projects | 2024 | Aggregation models; Weighted Linear Combination (WLC) method; cluster analysis. | From the literature and completed projects, 7 groups of factors, with a total of 56 factors, which influence the increase in the price and the extension of the deadline for the construction of infrastructure projects were identified. Every factor is quantified (price and number of days). Prevention measures were defined for the factors that have the greatest impact on increasing the costs and time of construction of infrastructure projects. |
Accepted Contract Amount [EUR] | Time for Completion [Days] | The Length of the Section [km] | ICP [EUR] | EoT [Days] | |
---|---|---|---|---|---|
Average | 27,278,833.36 | ~610 | 7.23 | 41,343,706.21 | ~743 |
Min | 3,283,504.45 | 120 | 0.17 | 1,537,082.86 | 0 |
Max | 74,738,676.05 | 900 | 21.40 | 194,166,387.13 | 2075 |
Design | 1.1 Non-compliance of the project with environmental conditions due to inappropriate design bases. |
1.2 Lack of details and technical specifications in the project documentation (insufficiently elaborated parts of the project documentation). | |
1.3 Complex design or inappropriate construction technology. | |
1.4 Non-compliance in parts of project documentation. | |
1.5 Delays in the production or changes in project documentation during execution. | |
1.6 Incorrect Bill of Quantities of works. | |
1.7 Insufficiently examined and imprecisely determined locations, as well as available quantities of materials in borrow pits. | |
1.8 Failure to provide adequate locations for deposit areas for excavated materials. | |
1.9 Unresolved collisions with existing infrastructure facilities (underground installations, pipelines, local roads, railways, etc.). | |
1.10 Inadequate design of riverbeds and storm water treatment. | |
1.11 Inapplicable project documentation for high cuts, including tunnel portals. | |
External | 2.1 Problems with property–legal relations (e.g., expropriation, etc.). |
2.2 Delay in obtaining permits and approvals from relevant authorities. | |
2.3 Problems with obtaining a use permit. | |
2.4 Local regulations regarding the construction of tunnels are insufficient. | |
2.5 Changes in laws and regulations. | |
2.6 Banks—compliance with the environmental and social requirements of each bank. | |
2.7 Exchange rate instability and resource price spikes. | |
2.8 New environmental restrictions or unforeseen circumstances (archeological sites, mines and explosives, etc.). | |
2.9 Exceptionally adverse weather conditions. | |
2.10 Force majeure (natural disasters, pandemic, epidemic, etc.). | |
Resource | 3.1 Labor shortage. |
3.2 Low productivity and unskilled labor force. | |
3.3 Lack of materials on the market. | |
3.4 Inadequate quality of materials. | |
3.5 Equipment failures and obsolete machinery. | |
3.6 Lack of equipment (mechanization). | |
Employer | 4.1 Delays in payment of Interim Payment Certificate by the Employer. |
4.2 Variation Order request. | |
4.3 Slow decision-making. | |
4.4 Poor communication between the Employer and other project participants. | |
4.5 Lack of funds or lengthy procedure for financing unforeseen works and Variation Orders. | |
4.6 Delay in handing over (parts of) the construction site to the contractor. | |
4.7 A long period of additional contracting for unforeseen and subsequent works (especially due to changes during implementation). | |
Contractor | 5.1 Re-execution of works due to errors or poor quality of the works performed. |
5.2 Poor financial condition of the contractor. | |
5.3 Inefficient planning and management of works on the construction site. | |
5.4 Inadequate experience of the contractor. | |
5.5 Irresponsible execution of works and jeopardizing the safety of other works. | |
5.6 The contractor entering the OFAC (Office of Foreign Assets Control) list of sanctioned persons and companies. | |
Engineer | 6.1 Lack of experience and expertise on the part of the engineer. |
6.2. Insufficient number of engineering team members. | |
6.3 Avoiding professional supervision to take a proactive role and issue instructions. | |
6.4 Delays in reviewing and certifying the performed works. | |
6.5 Delays in the review and approval of the Method Statement. | |
6.6 Delays in the review and approval of materials. | |
Project | 7.1 Inadequate duration of the project according to the contract. |
7.2 Inadequate or imprecise contract terms. | |
7.3 Unsolved Claims, Variations, and VEPs. | |
7.4 High complexity of the project (scope of works, topography, access restrictions, new technologies, etc.). | |
7.5 Disputes between different parties involved in the project during the implementation of the works. | |
7.6 Inadequate Cash Flow of the project. | |
7.7 Poor Contract Management of the project. | |
7.8 Termination of the contract. | |
7.9 Non-compliance of the contractor’s activities on adjacent construction sites. | |
7.10 Accidents on the construction site. |
Risk Group | Risks | (a) Average Number of Occurrences of Major Risk | (b) Average Number of Occurrences of Secondary Risk |
---|---|---|---|
Design | 1.1 | 10.54 | 18.64 |
1.2 | 3.57 | 2.32 | |
1.3 | 2.57 | 4.64 | |
1.4 | 7.04 | 10.54 | |
1.5 | 3.93 | 9.04 | |
1.6 | 3.75 | 3.21 | |
1.7 | 3.18 | 0.54 | |
1.8 | 2.57 | 0.68 | |
1.9 | 12.04 | 4.39 | |
1.10 | 8.25 | 2.39 | |
1.11 | 13.29 | 3.36 | |
External | 2.1 | 1.57 | 0.96 |
2.2 | 2.21 | 5.46 | |
2.3 | 2.43 | 0.82 | |
2.4 | 0.32 | 0.07 | |
2.5 | 1.75 | 0.82 | |
2.6 | 1.61 | 0.50 | |
2.7 | 1.21 | 0.36 | |
2.8 | 0.36 | 0.32 | |
2.9 | 0.18 | 0.54 | |
2.10 | 0.14 | ||
Resource | 3.1 | 5.46 | |
3.2 | 0.25 | 0.82 | |
3.3 | 0.07 | 0.07 | |
3.4 | 1.86 | 4.04 | |
3.5 | 0.07 | ||
3.6 | 0.07 | ||
Employer | 4.1 | 0.46 | 0.21 |
4.2 | 2.25 | 5.07 | |
4.3 | 0.18 | 0.21 | |
4.4 | 0.04 | ||
4.5 | 0.18 | 0.25 | |
4.6 | 0.32 | 0.79 | |
4.7 | 0.07 | ||
Contractor | 5.1 | 0.39 | 0.43 |
5.2 | 0.32 | 0.25 | |
5.3 | 0.11 | 0.11 | |
5.4 | 0.21 | ||
5.5 | 0.04 | 0.14 | |
5.6 | 0.21 | 0.04 | |
Engineer | 6.1 | 0.11 | 0.11 |
6.2. | 0.04 | ||
6.3 | 0.11 | ||
6.4 | 0.14 | ||
6.5 | 0.11 | 0.14 | |
6.6 | 0.21 | ||
Project | 7.1 | 0.29 | 0.29 |
7.2 | 1.64 | 2.71 | |
7.3 | 0.61 | 1.11 | |
7.4 | 1.82 | 1.61 | |
7.5 | 0.68 | 0.39 | |
7.6 | 0.14 | ||
7.7 | 0.25 | 0.29 | |
7.8 | 0.36 | 0.18 | |
7.9 | 2.82 | 0.79 | |
7.10 | 0.39 |
EoT [%] | ICP [%] | ||||
---|---|---|---|---|---|
Risk Group | Risks | Major Risk | Secondary Risk | Major Risk | Secondary Risk |
Design | 1.1 | 10.25 | 3.24 | 1.24 | 0.35 |
1.2 | 2.77 | 0.41 | 0.30 | ||
1.3 | 2.42 | 1.05 | 0.28 | ||
1.4 | 9.64 | 3.18 | 1.24 | 0.16 | |
1.5 | 29.57 | 2.34 | 2.91 | 0.43 | |
1.6 | 3.28 | 9.75 | 0.56 | ||
1.7 | 7.15 | 1.87 | 1.57 | 1.11 | |
1.8 | 2.39 | 1.07 | 0.51 | ||
1.9 | 20.27 | 2.52 | 0.33 | 0.21 | |
1.10 | 3.16 | 0.55 | 0.18 | ||
1.11 | 16.28 | 2.66 | 5.66 | 0.39 | |
External | 2.1 | 31.69 | 2.89 | 2.26 | 0.07 |
2.2 | 28.03 | 2.97 | 0.83 | 0.49 | |
2.3 | 2.53 | 0.36 | 0.31 | ||
2.4 | 4.31 | 0.17 | 0.01 | ||
2.5 | 2.90 | 0.94 | 0.05 | ||
2.6 | 2.81 | 1.12 | 0.67 | ||
2.7 | 2.57 | 2.10 | 10.84 | 0.37 | |
2.8 | 10.41 | 1.69 | 0.30 | 0.51 | |
2.9 | 4.09 | 3.40 | 1.49 | 0.57 | |
2.10 | 1.47 | 0.40 | |||
Resource | 3.1 | 0.35 | 0.18 | ||
3.2 | 0.35 | 2.46 | 0.18 | ||
3.3 | 2.93 | 0.50 | 0.78 | ||
3.4 | 3.44 | 1.13 | 0.18 | ||
3.5 | 1.63 | 0.02 | |||
3.6 | 1.63 | 0.01 | |||
Employer | 4.1 | 4.78 | 4.24 | 0.26 | 0.95 |
4.2 | 2.33 | 0.37 | 0.12 | ||
4.3 | 7.15 | 6.56 | 3.00 | 0.69 | |
4.4 | 0.56 | 0.03 | |||
4.5 | 4.11 | 0.06 | 0.02 | ||
4.6 | 24.17 | 3.81 | 3.46 | 0.33 | |
4.7 | 1.22 | 0.63 | |||
Contractor | 5.1 | 3.43 | 0.37 | 0.54 | |
5.2 | 6.97 | 2.63 | 2.61 | 3.99 | |
5.3 | 2.81 | 1.54 | 9.87 | 0.03 | |
5.4 | 3.23 | 0.20 | |||
5.5 | 1.01 | 1.24 | 0.34 | ||
5.6 | 4.83 | 2.17 | 7.56 | 3.55 | |
Engineer | 6.1 | 1.53 | 0.04 | 0.30 | |
6.2. | 8.12 | 0.03 | |||
6.3 | 2.75 | 0.35 | |||
6.4 | 1.88 | 0.02 | |||
6.5 | 2.10 | 0.29 | 0.09 | ||
6.6 | 3.10 | 0.35 | |||
Project | 7.1 | 2.22 | 0.93 | 0.13 | |
7.2 | 0.69 | 2.35 | 0.40 | 0.19 | |
7.3 | 0.03 | 2.28 | 0.30 | 0.76 | |
7.4 | 0.02 | 2.52 | 5.10 | 0.16 | |
7.5 | 0.33 | 2.71 | 3.75 | 0.29 | |
7.6 | 6.11 | 1.45 | |||
7.7 | 0.91 | 0.24 | 0.05 | ||
7.8 | 8.99 | 21.22 | 0.58 | ||
7.9 | 0.63 | 2.67 | 1.81 | 0.34 | |
7.10 | 1.64 | 0.81 |
Occurrences of Risk | Impact of Risk EoT | Impact of Risk ICP | ||||||
---|---|---|---|---|---|---|---|---|
Risk Group | Risks | Major | Secondary | Major Risk | Secondary Risk | Major Risk | Secondary Risk | Risk Score |
Design | 1.1 | 0.7931 | 1.0000 | 0.1486 | 0.2662 | 0.0301 | 0.0877 | 0.39 |
1.2 | 0.2686 | 0.1245 | 0.2276 | 0.0099 | 0.0752 | 0.12 | ||
1.3 | 0.1934 | 0.2489 | 0.1988 | 0.0255 | 0.0702 | 0.12 | ||
1.4 | 0.5297 | 0.5655 | 0.1398 | 0.2613 | 0.0301 | 0.0401 | 0.26 | |
1.5 | 0.2957 | 0.4850 | 0.4287 | 0.1923 | 0.0706 | 0.1078 | 0.26 | |
1.6 | 0.2822 | 0.1722 | 0.2695 | 0.2365 | 0.1404 | 0.18 | ||
1.7 | 0.2393 | 0.0290 | 0.1037 | 0.1537 | 0.0381 | 0.2782 | 0.14 | |
1.8 | 0.1934 | 0.0365 | 0.1964 | 0.0260 | 0.1278 | 0.10 | ||
1.9 | 0.9059 | 0.2355 | 0.2939 | 0.2071 | 0.0080 | 0.0526 | 0.28 | |
1.10 | 0.6208 | 0.1282 | 0.2597 | 0.0133 | 0.0451 | 0.18 | ||
1.11 | 1.0000 | 0.1803 | 0.2360 | 0.2186 | 0.1373 | 0.0977 | 0.31 | |
External | 2.1 | 0.1181 | 0.0515 | 0.4595 | 0.2375 | 0.0548 | 0.0175 | 0.16 |
2.2 | 0.1663 | 0.2929 | 0.4064 | 0.2440 | 0.0201 | 0.1228 | 0.21 | |
2.3 | 0.1828 | 0.0440 | 0.2079 | 0.0087 | 0.0777 | 0.09 | ||
2.4 | 0.0241 | 0.0038 | 0.3541 | 0.0041 | 0.0025 | 0.06 | ||
2.5 | 0.1317 | 0.0440 | 0.2383 | 0.0228 | 0.0125 | 0.07 | ||
2.6 | 0.1211 | 0.0268 | 0.2309 | 0.0272 | 0.1679 | 0.10 | ||
2.7 | 0.0910 | 0.0193 | 0.0373 | 0.1726 | 0.2630 | 0.0927 | 0.11 | |
2.8 | 0.0271 | 0.0172 | 0.1509 | 0.1389 | 0.0073 | 0.1278 | 0.08 | |
2.9 | 0.0135 | 0.0290 | 0.0593 | 0.2794 | 0.0361 | 0.1429 | 0.09 | |
2.10 | 0.0075 | 0.1208 | 0.1003 | 0.04 | ||||
Resource | 3.1 | 0.2929 | 0.0288 | 0.0451 | 0.06 | |||
3.2 | 0.0188 | 0.0440 | 0.0288 | 0.0597 | 0.0451 | 0.03 | ||
3.3 | 0.0053 | 0.0038 | 0.2408 | 0.0121 | 0.1955 | 0.08 | ||
3.4 | 0.1400 | 0.2167 | 0.2827 | 0.0274 | 0.0451 | 0.12 | ||
3.5 | 0.0038 | 0.1339 | 0.0050 | 0.02 | ||||
3.6 | 0.0038 | 0.1339 | 0.0025 | 0.02 | ||||
Employer | 4.1 | 0.0346 | 0.0113 | 0.0693 | 0.3484 | 0.0063 | 0.2381 | 0.12 |
4.2 | 0.1693 | 0.2720 | 0.1915 | 0.0090 | 0.0301 | 0.11 | ||
4.3 | 0.0135 | 0.0113 | 0.1037 | 0.5390 | 0.0728 | 0.1729 | 0.15 | |
4.4 | 0.0021 | 0.0460 | 0.0075 | 0.01 | ||||
4.5 | 0.0135 | 0.0134 | 0.3377 | 0.0015 | 0.0050 | 0.06 | ||
4.6 | 0.0241 | 0.0424 | 0.3504 | 0.3131 | 0.0839 | 0.0827 | 0.15 | |
4.7 | 0.0038 | 0.1002 | 0.1579 | 0.04 | ||||
Contractor | 5.1 | 0.0293 | 0.0231 | 0.2818 | 0.0090 | 0.1353 | 0.08 | |
5.2 | 0.0241 | 0.0134 | 0.1011 | 0.2161 | 0.0633 | 1.0000 | 0.24 | |
5.3 | 0.0083 | 0.0059 | 0.0407 | 0.1265 | 0.2394 | 0.0075 | 0.07 | |
5.4 | 0.0113 | 0.2654 | 0.0501 | 0.05 | ||||
5.5 | 0.0030 | 0.0075 | 0.0830 | 0.0301 | 0.0852 | 0.03 | ||
5.6 | 0.0158 | 0.0021 | 0.0700 | 0.1783 | 0.1834 | 0.8897 | 0.22 | |
Engineer | 6.1 | 0.0083 | 0.0059 | 0.1257 | 0.0010 | 0.0752 | 0.04 | |
6.2. | 0.0021 | 0.6672 | 0.0075 | 0.11 | ||||
6.3 | 0.0059 | 0.2260 | 0.0877 | 0.05 | ||||
6.4 | 0.0075 | 0.1545 | 0.0050 | 0.03 | ||||
6.5 | 0.0083 | 0.0075 | 0.1726 | 0.0070 | 0.0226 | 0.04 | ||
6.6 | 0.0113 | 0.2547 | 0.0877 | 0.06 | ||||
Project | 7.1 | 0.0218 | 0.0156 | 0.1824 | 0.0226 | 0.0326 | 0.05 | |
7.2 | 0.1234 | 0.1454 | 0.0100 | 0.1931 | 0.0097 | 0.0476 | 0.09 | |
7.3 | 0.0459 | 0.0595 | 0.0004 | 0.1873 | 0.0073 | 0.1905 | 0.08 | |
7.4 | 0.1369 | 0.0864 | 0.0003 | 0.2071 | 0.1237 | 0.0401 | 0.10 | |
7.5 | 0.0512 | 0.0209 | 0.0048 | 0.2227 | 0.0910 | 0.0727 | 0.08 | |
7.6 | 0.0075 | 0.5021 | 0.3634 | 0.15 | ||||
7.7 | 0.0188 | 0.0156 | 0.0748 | 0.0058 | 0.0125 | 0.02 | ||
7.8 | 0.0271 | 0.0097 | 0.7387 | 0.5148 | 0.1454 | 0.24 | ||
7.9 | 0.2122 | 0.0424 | 0.0091 | 0.2194 | 0.0439 | 0.0852 | 0.10 | |
7.10 | 0.0209 | 0.1348 | 0.2030 | 0.06 |
ICP | ||||||
---|---|---|---|---|---|---|
Major Risk | Secondary Risk | |||||
MAE | MSE | R2 | MAE | MSE | R2 | |
1.1 | 0.16 | 0.0186 | 0.9578 | 0.06 | 0.0052 | 0.9622 |
1.11 | 0.18 | 0.0349 | 0.04 | 0.0034 | ||
1.9 | 0.11 | 0.0072 | 0.07 | 0.0068 | ||
1.5 | 0.36 | 0.0729 | 0.08 | 0.0095 | ||
1.4 | 0.48 | 0.0858 | 0.13 | 0.0124 |
EoT | ||||||
---|---|---|---|---|---|---|
Major Risk | Secondary Risk | |||||
MAE | MSE | R2 | MAE | MSE | R2 | |
1.1 | 0.43 | 0.1647 | 0.9486 | 0.19 | 0.0384 | 0.9602 |
1.11 | 0.62 | 0.4141 | 0.12 | 0.0105 | ||
1.9 | 0.89 | 1.3084 | 0.09 | 0.0092 | ||
1.5 | 0.92 | 1.3481 | 0.21 | 0.0184 | ||
1.4 | 0.77 | 1.0238 | 0.26 | 0.0242 |
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Senić, A.; Dobrodolac, M.; Stojadinović, Z. Development of Risk Quantification Models in Road Infrastructure Projects. Sustainability 2024, 16, 7694. https://doi.org/10.3390/su16177694
Senić A, Dobrodolac M, Stojadinović Z. Development of Risk Quantification Models in Road Infrastructure Projects. Sustainability. 2024; 16(17):7694. https://doi.org/10.3390/su16177694
Chicago/Turabian StyleSenić, Aleksandar, Momčilo Dobrodolac, and Zoran Stojadinović. 2024. "Development of Risk Quantification Models in Road Infrastructure Projects" Sustainability 16, no. 17: 7694. https://doi.org/10.3390/su16177694