Citation :

Diksha Sanjay Jadhav, Amey Deepak Katdare, "Empirical Analysis of Time-Cost Risk Coupling in Construction Projects using FMEA, Correlation, and Regression Models," International Journal of Civil Engineering, vol. 13, no. 7, pp. 145-160, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I7P109

Abstract

Construction project time and cost overruns continue to pose a significant performance challenge, hindering financial viability, delivery certainty, and stakeholder confidence. Although time and cost overruns are common, treating them as separate issues often diminishes the effectiveness of risk mitigation strategies. Current research is often limited to either a single aspect of delay or cost escalation, or it relies on qualitative rankings and complex predictive models that lack substantial empirical evidence regarding the general causes and systematic transformation of time-based risks into cost overruns. This research focuses on empirically studying the coupling of time and cost risks in construction projects by combining Failure Mode and Effects Analysis (FMEA) with statistical correlation and FMEA with statistical regression models, and conceptually relating the results to Earned Value Management (EVM) indicators. Major factors that cause internal and external delays are identified and quantified in terms of FMEA-derived Risk Priority Numbers for time and cost (Time-RPN and Cost-RPN). The magnitude of factors with regard to risk and time is tested with a Pearson correlation study, and regression analysis is conducted factor-wise to determine the contribution of each factor while predicting the cost risk and the contribution of time risk. The results reveal that the time–cost coupling is quite beneficial, and statistically significant results are obtained for all significant risk factors of Delay, thus validating the idea of schedule-related risks as cost escalation indicators. From the results of the regression analysis, it can be seen that there are risks related to time that significantly affect the variability of the project costs, in particular related to the availability of labor, time for design changes, and coordination problems. The robustness of the proposed framework was verified through the sensitivity analysis, with results being very close (ρ = 0.952 - 0.988) for different alternative weighting scenarios. The relationship between an EVM indicator and the schedule inefficiencies is now empirically proven and can be used to act sooner before any financial consequences may occur. The transparent framework developed in this study is based on an empirical process and can be used to establish a time-driven risk governance process that assists in schedule control, serving as a proactive measure to avoid cost overruns in construction projects.

Keywords

Construction risk management, Correlation analysis, Earned Value Management (EVM), Failure Mode and Effects Analysis (FMEA), Regression analysis, Time–cost coupling.

References

1. Abdorreza Rezaee Arjroody et al., “Accurate Estimation of Cost and Time Utilizing Risk Analysis and Simulation (Case Study: Road Construction Projects in Iran),” International Journal of Construction Management, vol. 24, no. 1, pp. 19-30, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Augustin Purnus, and Constanta-Nicoleta Bodea, “Correlation between Time and Cost in a Quantitative Risk Analysis of Construction Projects,” Procedia Engineering, vol. 85, pp. 436-445, 2014.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Paúl Urgilés, Juan Claver, and Miguel A. Sebastián, “Methods for Quantitative Risks Analysis of Cost and Deadline Overruns in Complex Projects,” Procedia Manufacturing, vol. 41, pp. 658-665, 2019.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Vishal Kumar Gupta, and Jitesh J. Thakkar, “A Quantitative Risk Assessment Methodology for Construction Project,” Sādhanā, vol. 43, no. 7, 2018.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Farnad Nasirzadeh, Mostafa Khanzadi, and Mahdi Rezaie, “Dynamic Modeling of the Quantitative Risk Allocation in Construction Projects,” International Journal of Project Management, vol. 32, no. 3, pp. 442-451, 2014.
   [CrossRef] [Google Scholar] [Publisher Link]
6. M. Ammar Alzarrad et al., “Fuzzy Multicriteria Decision‐Making Model for Time‐Cost‐Risk Trade‐Off Optimization in Construction Projects,” Advances in Civil Engineering, vol. 2019, no. 1, pp. 1-7, 2019.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Edyta Plebankiewicz, Krzysztof Zima,  and Damian Wieczorek, “Modelling of Time, Cost and Risk of Construction with Using Fuzzy Logic,” Journal of Civil Engineering and Management, vol. 27, no. 6, pp. 412-426, 2021.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Alvaro Julio Cuadros, Nicolle Alexandra Cruces Arévalo, and Claudia Ortiz, “Quantitative Risk Analysis for Construction Projects Considering Risks Correlations and Fuzzy Logic,” UIS Engineering Journal, vol. 23, no. 1, pp. 127-140, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Mohammad Amin Ashtari et al., “Cost Overrun Risk Assessment and Prediction in Construction Projects: A Bayesian Network Classifier Approach,” Buildings, vol. 12, no. 10, pp. 1-23, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Yonatan Getachew Zegeye, and Mohammadzen Hasan Darsa, “Potential Risk Factors Affecting Cost and Schedule Performance in the Case of Construction Projects in Dire Dawa City Administration and Harari Region, Ethiopia,” Building Engineering, vol. 3, no. 1, pp. 1-28, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
11. John Patrick Fitzsimmons et al., “Construction Schedule Risk Analysis – A Hybrid Machine Learning Approach,” Journal of Information Technology in Construction, vol. 27, pp. 70-93, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
12. G.B.S. Alekhya, K. Shashikanth, and M. Anjaneya Prasad, “Risk Assessment of Cost Overrun using Fuzzy Logic Model,” Materials Today: Proceedings, vol. 62, pp. 1803-1810, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Osama Moselhi, and Mohammadjavad Arabpour Roghabadi, “Risk Quantification using Fuzzy-Based Monte Carlo Simulation,” Journal of Information Technology in Construction, vol. 25, pp. 87-98, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Dai Tran, and Sai Bypaneni, “Impact of Correlation on Cost-Risk Analysis for Construction Highway Projects,” Construction Research Congress 2016, pp. 668-678, 2016.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Mohsen Ahmadi et al., “Comprehensive Risk Management using Fuzzy FMEA and MCDA Techniques in Highway Construction Projects,” Journal of Civil Engineering and Management, vol. 23, no. 2, pp. 300-310, 2017.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Alaa Mohsin Mahdi, and Kadhim Raheim Erzaij, “Developing ANFIS-FMEA Model for Assessment and Prioritization of Potential Trouble Factors in Iraqi Building Projects,” Open Engineering, vol. 14, no. 1, pp. 1-15, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Ahmed Gouda Mohamed, Mohamed Helmy Ammar, and Mohamed Nabawy, “Risks Assessment Using Structural Equation Modeling: Mega Housing Projects Construction in Egypt,” International Journal of Construction Management, vol. 23, no. 16, pp. 2717-2728, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Pooja Gupta, and Nageswara Rao Atyam, Enhancing Risk Assessment and Decision-Making Efficiency in Project Management by Enabling Predictive Analytics Using Artificial Intelligence (AI) And Machine Learning (ML) Technologies, Business Analytics and Intelligence: Driving Strategy with Data, QTanalytics, pp. 322-342, 2025.
    [CrossRef] [Publisher Link]
19. Arik Kurniawati, and Surya Wisena, “Integrate WBS and Forecast Task Completion in Construction Projects using Data Analytics,” 2023 International Conference on Technology, Engineering, and Computing Applications (ICTECA), Semarang, Indonesia, pp. 1-6, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Xiaosen Huo, Tong Hao, and Liudan Jiao, “Critical Risk Factors of Public Building Green Retrofit Projects- An Empirical Study in Chongqing, China,” Journal of Asian Architecture and Building Engineering, vol. 24, no. 3, pp. 1487-1499, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Gopinath Selvam et al., “Duration and Resource Constraint Prediction Models for Construction Projects Using Regression Machine Learning Method,” Engineering, Construction and Architectural Management, vol. 32, no. 9, pp. 5743-5763, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
22. Rana Jabbar Kasid Jalhoom, and Ahmed Mohammed Raoof Mahjoob, “A Framework for Construction-Related Risks by integrating the Fuzzy Grey Comprehensive Evaluation Method (FGCE),” Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 16736-16739, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
23. Zaid B. OWAIS, “Construction Projects Duration Derivatives, Correlations Analysis Justified by Frequency Count and Questionnaire,” SEA - Practical Application of Science, vol. 12, no. 36, pp. 229-241, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
24. N. Sai Krishna, and S.M. Renuka, “Scheduling Time and Cost by Integrating Quality and Risk in Construction Projects,” Sustainable Innovations in Construction Management, vol. 388, pp. 191-201, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
25. Xin Zou, and Lihui Zhang, “Fuzzy Repetitive Scheduling for Construction Projects Considering Risk Preferences and Time-Cost Tradeoffs,” Engineering, Construction and Architectural Management, vol. 33, no. 2, pp. 1284-1317, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
26. Meisam Mahboubi Niazmandi et al., “An Integrated Risk and Productivity Assessment Model for Public–Private Partnership Projects using Fuzzy Inference System,” Decision Analytics Journal, vol. 10, pp. 1-16, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
27. Peipei Wang et al., “A Contingency Approach for Time-Cost Trade-Off in Construction Projects Based on Machine Learning Techniques,” Engineering, Construction and Architectural Management, vol. 31, no. 11, pp. 4677-4695, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
28. Saleh Abu Dabou, Tareq Zadeh, and Fakhariya Ibrahim, “A Failure Mode, Effects and Criticality Analysis-Based Method for Formwork Assessment and Selection in Building Construction,” International Journal of Building Pathology and Adaptation, vol. 42, no. 5, pp. 1019-1040, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
29. Iman Youssefi, and Tolga Celik, “Comparative Analysis between Different Risk Score Calculation Approaches,” Engineering, Construction and Architectural Management, vol. 31, no. 10, pp. 4099-4124, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
30. Abhishek Rustagi, Assessment of Failure Mode and Effect Analysis (FMEA) with Fuzzy Logic for Mixed-Use Construction, Building Construction and Technology, pp. 99-111, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
31. Shakib Zohrehvandi et al., “A Fuzzy Project Buffer Management Algorithm: A Case Study in the Construction of a Renewable Project,” International Journal of Construction Management, vol. 23, no. 12, pp. 2134-2143, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
32. Abolfazl Alvand et al., “Identification and Assessment of Risk in Construction Projects using the Integrated FMEA-SWARA-WASPAS Model Under Fuzzy Environment: A Case Study of a Construction Project in Iran,” International Journal of Construction Management, vol. 23, no. 3, pp. 392-404, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
33. Hubert Anysz, and Magdalena Dąbrowska, “The Risk Indicators of Construction Projects’ Cost Overruns Assessed with PCA, Decision Trees, and Pearson’s Correlations,” Russian-Polish-Slovak Seminar Theoretical Foundation of Civil Engineering, vol. 189, pp. 424-432, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
34. Fábio Henrique de Souza et al., “Prioritizing Risks with Composition of Probabilistic Preferences and Weighting of FMEA Criteria for Fast Decision-Making in Complex Scenarios,” International Journal of Managing Projects in Business, vol. 15, no. 4, pp. 572-594, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
35. Debasis Sarkar, and Manvinder Singh, “Risk Analysis by Integrated Fuzzy Expected Value Method and Fuzzy Failure Mode and Effect Analysis for an Elevated Metro Rail Project of Ahmedabad, India,” International Journal of Construction Management, vol. 22, no. 10, pp. 1818-1829, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
36. Ali Beiki Ashkezari et al., “Selecting an Appropriate Configuration in a Construction Project Using a Hybrid Multiple Attribute Decision Making and Failure Analysis Methods,” Buildings, vol. 12, no. 5, pp. 1-30, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
37. Carlos Araújo-Rey, and Miguel A. Sebastián, “An Approach to the Analysis of Causes of Delays in Industrial Construction Projects through Planning and Statistical Computing,” Sustainability, vol. 13, no. 7, pp. 1-21, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
38. Mohammad Khalilzadeh et al., “Hybrid Fuzzy MCDM and FMEA Integrating with Linear Programming Approach for the Health and Safety Executive Risks: A Case Study,” Journal of Modelling in Management, vol. 16, no. 4, pp. 1025-1053, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
39. Suriyan Jomthanachai, Wai-Peng Wong, and Chee-Peng Lim, “An Application of Data Envelopment Analysis and Machine Learning Approach to Risk Management,” IEEE Access, vol. 9, pp. 85978-85994, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
40. Tianqi Wang et al., “Time–Cost–Quality Trade-Off Analysis for Planning Construction Projects,” Engineering, Construction and Architectural Management, vol. 28, no. 1, pp. 82-100, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
41. Farman Afzal et al., “Cost-Risk Contingency Framework for Managing Cost Overrun in Metropolitan Projects: Using Fuzzy-AHP and Simulation,” International Journal of Managing Projects in Business, vol. 13, no. 5, pp. 1121-1139, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
42. hunguang Chang, Xi Wu, and Xin Yan, “Multiobjective Optimization of Safety Risk of Prefabricated Building Construction Considering Risk Correlation,” Mathematical Problems in Engineering, vol. 2020, no. 1, pp. 1-11, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
43. Farhad Hosseinzadeh et al., “Fuzzy Combinatorial Optimization in Four-Dimensional Tradeoff Problem of Cost-Time-Quality-Risk in One Dimension and in the Second Dimension of Risk Context in Ambiguous Mode,” Engineering Computations, vol. 37, no. 6, pp. 1967-1991, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
44. Guofeng Ma, and Ming Wu, “A Big Data and FMEA-based Construction Quality Risk Evaluation Model Considering Project Schedule for Shanghai Apartment Projects,” International Journal of Quality & Reliability Management, vol. 37, no. 1, pp. 18-33, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
45. Hyeon-ae Jang, and Seungsik Min, “Time-Dependent Probabilistic Model for Hierarchical Structure in Failure Mode and Effect Analysis,” Applied Sciences, vol. 9, no. 20, pp. 1-17, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
46. Usama Hamed Issa, Salah Attia Mosaad, Mohamed Salah Hassan, “A Model for Evaluating the Risk Effects on Construction Project Activities,” Journal of Civil Engineering and Management, vol. 25, no. 7, pp. 687-699, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
47. Adel Alshibani et al., “Fuzzy Logic-Based Method for Forecasting Project Final Cost,” Buildings, vol. 14, no. 12, pp. 1-22, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
48. Abroon Qazi, and Mecit Can Emre Simsekler, “Risk Assessment of Construction Projects using Monte Carlo Simulation,” International Journal of Managing Projects in Business, vol. 14, no. 5, pp. 1202-1218, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
49. Kun Tian et al., “Artificial Intelligence in Risk Management within the Realm of Construction Projects: A Bibliometric Analysis and Systematic Literature Review,” Journal of Innovation & Knowledge, vol. 10, no. 3, pp. 1-25, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
50. Sanjay Ramdas Bauskar et al., “Predictive Analytics for Project Risk Management Using Machine Learning,” Journal of Data Analysis and Information Processing, vol. 12, no. 4, pp. 566-580, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
51. Jelena M. Andrić et al., “Investigating the Possible Regression Functions for Modelling Delays in Infrastructure Projects in South Asia,” KSCE Journal of Civil Engineering, vol. 29, no. 9, pp. 1-12, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]