Empirical Analysis of Time-Cost Risk Coupling in Construction Projects using FMEA, Correlation, and Regression Models

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.