Probabilistic parameter estimation and reliability assessment of a simulated sheet pile wall system

Abstract

This paper investigates the impact of structural health monitoring (SHM) on the failure probability of a simulated sheet pile wall system. We use Bayesian statistics to estimate non-directly observable soil parameters from measured structural responses. These estimates are used to calculate the failure probability of the structure against serviceability and ultimate limit states before and after measurements. We study the impact of sensor installation time and the contribution of various sensor types to the change in failure probability. Nested sampling is used to compute the posterior distributions and to estimate the failure probabilities. The results show that SHM largely decreases the failure probability estimates compared to the prior value. The largest failure probability reduction is obtained when the monitoring starts right after the sheet pile wall is driven into the ground: about 1017× and larger reductions for the considered ultimate limit state. With each delay in the sensor installation time, the failure probability reduction can substantially decrease, in some cases 103× and larger reductions are observed. The results imply that SHM coupled with Bayesian statistics is a promising approach to substantially reduce our uncertainty in modeling hydraulic structures and, in turn, to increase their calculated safety.