Study of the Damage Risk Reduction by Controlling Evaluation Accuracy using a Hierarchical Bayesian Model

Abstract

This study concerned with a risk evaluation method and a risk reduction method for maintenance activity using the identification results of damage identification using inverse problem analysis. Regression analysis is the most commonly used method for damage identification by inverse problem analysis. Linear regression analysis is easy to compute, but the output is a fixed value, and the predicted mean value is given as the solution. However, when evaluating the reliability of a structure, it is necessary to consider the distribution of diagnostic error because the failure of the structure is caused by low-probability events. Therefore, the author's research group is proposing a method to estimate the distribution of occurrence rates of damage parameters and calculate probability of failure by deriving regression coefficients as a distribution using Bayesian estimation as the damage identification results. In addition, in order to construct a method for quantitative maintenance decision making, a risk quantification method as the result of damage identification is proposed. Overestimating damage that is sufficiently smaller than the expected failure size will not cause failure, and underestimating damage that is sufficiently large will not cause failure. In other words, these areas do not require very high accuracy when maintenance is performed using damage identification results. In this study, a method to reduce risk and probability of failure by controlling the estimation accuracy in arbitrary regions using a generalized linear mixed model (GLMM), which is one of the hierarchical Bayesian models, is investigated.