Integrating risk management's best practices to estimate deep excavation projects’ time and cost

Abstract

PurposeThis study provides an integrated risk-based cost and time estimation approach for deep excavation projects. The purpose is to identify the best practices in recent advances of excavation risk analysis (RA) and integrate them with traditional cost and time estimation methods.Design/methodology/approachThe implemented best practices in this research are as follows: (1) fault-tree analysis (FTA) for risk identification (RI); (2) Bayesian belief networks (BBNs), fuzzy comprehensive analysis and Monte Carlo simulation (MCS) for risk analysis; and (3) sensitivity analysis and root-cause analysis (RCA) for risk response planning (RRP). The proposed approach is applied in an actual deep excavation project in Tehran, Iran.FindingsThe results show that the framework proposes a practical approach for integrating the risk management (RM) best practices in the domain of excavation projects with traditional cost and time estimation approaches. The proposed approach can consider the interrelationships between risk events and identify their root causes. Further, the approach engages different stakeholders in the process of RM, which is beneficial for determining risk owners and responsibilities.Originality/valueThis research contributes to the project management body of knowledge by integrating recent RM best practices in deep excavation projects for probabilistic estimation of project time and cost.

Sustainability risk assessment in mega construction projects

“…As an illustration, Alashwal and Chew (2017) utilised a simulation technique, Monte Carlo simulation, to model uncertainties regarding the cost estimation of construction projects. Furthermore, Heravi et al. (2022) conducted a probabilistic assessment of deep excavation projects' time and cost with the contribution of Bayesian belief networks (BBNs), fuzzy comprehensive analysis and Monte Carlo simulation.…”