Cost Estimation for Underwater Tunnel Projects based on Uncertainty and Risk Analysis

• Main Author: Arestegui Carvajal, Miguel Angel
• Format: Others
• Language: English
• Published: Norges teknisk-naturvitenskapelige universitet, Institutt for industriell økonomi og teknologiledelse 2014
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-26996

Cost estimation is a complex and critical process, particularly during pre-investment phases of large undersea tunnel projects, where major decisions must be made under a high level of uncertainty. The high level of uncertainty regarding geological and construction performance aspects, as well as the occurrence of undesirable risk events may certainly affect the actual execution cost, making cost estimation a difficult task to be performed during the early phases.This work presents a cost estimation model based on uncertainty and risk analysis that may help project organisations to obtain more realistic cost estimates. The specific model was designed for Drill and Blast excavation method, and it is focused on the cost estimation of the tunnelling activities. Through standard project management tools, this model estimates the total tunnelling cost (CTT) as a random function of the normal (CNT) and extraordinary tunnelling cost (CET). The model assumes that normal cost is controlled by geological and construction aspects, while the extraordinary tunnelling cost may be derived for the occurrence of undesirable events. Both are modelled as random processes and integrated in @Risk, which allows performing Monte Carlo Simulations (MCS) and obtain the final cost distributions (PDF).The model was tested in a specific case study, and the results demonstrate the suitability of the model for determine the total tunnelling cost. Even though the model has demonstrated to be valid, the model robustness and accuracy may be improved by more advanced research in areas related to rock support and water inflow control. Finally, the results have confirmed that the integration of stochastic and driver-based and risk management tools may provide a powerful tool to improve the pre investment decision process of undersea tunnel projects