Bayesian Updating Application into Simulation in the North Edmonton Sanitary Trunk Tunnel Project

Abstract

Simulation models are built on assumptions, approximations, and estimates. Repetitive long-term projects such as tunnel construction provide opportunities to finetune approximations based on input from actual project progress. Bayesian updating techniques are an effective approach for improving the quality of simulation input and output based on what has already been observed. This paper presents a case study in which Bayesian techniques were applied to a simulation model of an actual tunnel project, the North Edmonton Sanitary Trunk. The study shows that using Bayesian techniques greatly improves the quality of projections. The novelty of this work includes the enhancement of the application of Bayesian updating techniques, the demonstration of simulation applications with a fully monitored tunneling project, and the demonstration of the extent of improvement to planning predictions from the use of actual data and the Bayesian updating techniques.