Risk Management in Tunneling Projects

QC240410

Abstract

Cost overruns and schedule delays are frequently observed occurrences in the construction of transport infrastructure projects. Such phenomena lead to the mismanagement of significant amounts of both public and private resources.An examination of the literature reveals that uncertainty stands out as one of the potential primary causes of cost overruns and schedule delays. To address the impact of uncertainty on time and cost estimations in transport infrastructure projects, probabilistic approaches can be employed. 

In this doctoral thesis, first a conceptual risk model has been formulated specifically for the purpose of enhancing time and cost estimations in tunneling projects. This risk model serves as a tool to scrutinize and contrast existing probabilistic time and cost estimation models for tunnel projects, aiming to identify potential areas for improvement. Furthermore, the conceptual model is utilized to delve into the factors influencing the accuracy of subjective assessments regarding the input parameters in time estimation models. It also explores methods for incorporating the role of tunneling phases into the subjective assessment of these input parameters.

Then, enhancements and updates are introduced to the existingKTH model for time and cost estimation in tunneling projects. This model primarily targets three main sources of uncertainty: variability in construction performance, geological uncertainties, and the potential incidence of disruptive events. The analysis and improvements related to modelling of construction performance involve three sequential steps. In the first step, the construction process is modeled using the work breakdown structure (WBS), enabling a more realistic assessment of tunneling time. Subsequently, in the second step, PERT distributions are employed to model the uncertainty in the duration of unit activities, compared to the commonly used triangular distributions. The third step involves a detailed examination of a real tunnelling project's data to identify components contributing to construction performance variability for unit activities. This analysis pinpoints three main components: typical performance variability, minor performance delays, and minor machinery delays. These components are integrated into the KTH model, resulting in its further update concerning construction performance variability. 

A novel approach is introduced into the KTH model by leveraging the Metropolis-Hastings (MH) algorithm within the framework of Markov Chain Monte Carlo (MCMC) simulation to address geological uncertainties along the tunnel route. This method facilitates round-by-round simulation of the tunneling process and allows the model to accommodate uncertainty in the critical path for tunneling projects involving multiple headings. These enhancements aim to improve decision-making processes and mitigate risks associated with schedule delays and cost overruns. Additionally, the magnitude of disruptive events are now modeled as stochastic variables, an improvement on the original version of the KTH model.

urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-345073