IMPROVING TUNNELING SIMULATION USING BAYESIAN UPDATING AND HIDDEN MARKOV CHAINS

Ground conditions remain an uncertain factor in tunneling projects, complicating the ability of practitioners to reliably estimate project productivity and, in turn, duration. This study proposes a Bayesian-based approach to incorporate real-time project data into simulation-based ground prediction models to improve prediction accuracy. Changes in ground conditions are modeled using a Hidden Markov Model, which is updated with actual project data using the Baum-Welch algorithm. The prediction model is then incorporated in Simphony. NET to enhance simulation of tunneling construction operations. A case study conducted in Edmonton, Canada, demonstrates that the proposed approach is capable of incorporating real-time data in a manner that resulted in enhanced duration prediction accuracy.