Time and cost estimation of tunnelingprojects is usually performed in a deterministic manner.However, because the deterministic approach isnot capable of dealing with uncertainty, probabilisticmethods have been developed over the years to betteraccount for this problem. Three models of this typeare the Decision Aids for Tunneling (DAT) and twomodels developed at KTH Royal Institute of Technologyand the Czech Technical University in Prague.To conduct a probabilistic time and cost estimation,it is important to understand and account for not onlythe uncertain factors that affect the project time andcost but also the involved parties’ different interestsand contractual responsibilities. This paper developsa risk model for the specific purpose of time andcost estimation of tunneling projects. In light of thismodel, the practical application of the three probabilisticmodels is discussed from a risk-aware decisionmaker’sperspective. The acquired insights can behelpful in increasing the experts’ risk-awareness inmodeling time and cost of tunneling projects.

Probabilistic time estimation is an essential part of proper risk management in tunneling projects. In recent decades, several models have been developed for this purpose, one of which was developed by Isaksson and Stille (Rock Mech Rock Eng 38:373–398, 2005). In this paper, Isaksson and Stille’s probabilistic time and cost estimation model was improved and then applied to estimate the total tunneling time of the headrace tunnel in the Uri hydropower project in India. The improvements allow the user to more accurately account for different types of geological features and disruptive events. The result of the estimation is a distribution of tunneling time. The outcome illustrates how a proper understanding of the geological setting of the project and its effect on construction performance can contribute to effective risk management. 

The PERT distribution may be a suitable distribution for modeling activities’ duration in probabilistic time estimation of tunnel projects as it puts more emphasis on the mean value of the distribution. In this paper, we compared the outcome of time estimations for a tunnel, using the triangular and PERT distributions for modeling the uncertainty of activities’ duration. The results indicate that the choice of the distribution affects the total estimated time considerably. In addition, the skewness of the distribution also affects the results of estimation meaning that realistic assessment of the parameters of the distributions is important.

Budget overrun and schedule delay of infrastructure projects result in mismanagement of huge amounts of resources. Studies show that accounting for uncertainties in time and cost estimation of such projects can help the decision-makers in better understanding the involved risks. Several probabilistic models have been developed during the last two decades to facilitate such estimations. A common aspect of all these models is the subjective assessment of unit activities’ duration, which affects the accuracy of the estimated time and cost significantly. Despite this, the mechanism governing the variability of unit activities’ duration has seldom been studied. Thus, this paper focuses on addressing this gap by using a unique set of data from a tunnel project. The variability in unit activities’ duration (construction performance variability) is governed by three main components: typical performance variability, minor machinery delays, and minor performance delays. Using these components, a novel method for modeling construction performance variability in probabilistic time estimation of tunneling projects is proposed. The application of the proposed method is demonstrated through a case example with a discussion on its important aspects, advantages, and limitations. The findings of this paper offer a resource to improve the accuracy of time estimation for tunneling projects.

Transport infrastructure projects frequently encounter challenges such as schedule delays and cost overruns, leading to substantial misallocation of public or private resources. These issues are often exacerbated by uncertainties in time and cost estimation outcomes. To address this, various models have been developed in recent years to enable probabilistic estimation for tunneling projects, considering uncertainties. However, the impact of uncertainties on the critical path and its implications on time and cost estimations have not been explored for network underground structures encompassing multiple construction paths. In this study, we update the KTH time and cost estimation model using the Markov Chain Monte Carlo (MCMC) method. This updated model enables simultaneous round-by-round construction simulation across all paths in network underground structures. Consequently, it accommodates uncertainty in the critical path and its influence on time estimation outcomes. Additionally, the updated model introduces an innovative technique to model geological uncertainties along tunnel routes, thereby contributing to the field's diversity. Practical application of the updated model is showcased using the Uri Hearace tunnel as an illustrative example. The paper also delves into the practical implications of the findings from a decision-maker's standpoint, as well as discussing the model's limitations.

Probabilistic time and cost estimation is a useful approach to assist decision-making under uncertainty concerning budget and duration of tunneling projects. In recent years, several models have been developed that can be used for this purpose, where the subjective assessments of the values of input parameters conducted by estimators are of crucial importance for the accuracy of the estimated time and cost. However, the factors that can affect the accuracy of the subjective assessments are not studied thoroughly in the literature. Thus, we discuss these affecting factors from the perspective of a conceptual risk model that can be used for the specific purpose of time and cost estimation of tunneling projects. In addition, by defining three pre-operational phases in the lifetime of tunneling projects (initiation, planning, and construction), we explain how these phases come into play when assessing the input parameters of the models.