Structural Health Monitoring Accounting for Thermal Variability and Damage using Approximate Bayesian Computation

Abstract

In structural engineering, damage is characterized as a change in material property, boundary condition, or geometry. The changes in these properties/parameters lead to a change in the measured response. The difference in measurements can be due to actual damage in the member (due to crack formation, corrosion of rebars, or crushing of concrete), or it might be due to temperature variations while making measurements. Temperature variability significantly affects the accuracy of structural health monitoring strategies in quantifying structural damage. Performing damage detection without isolating/incorporating these variations can lead to false damage detection, i.e., the undamaged structure can be detected as damaged. Hence, a method is required to isolate the effect of these variabilities while detecting damage. Researchers have developed methods to analyze and separate the effects of environmental variability from damageinduced changes in the measures. The main two approaches are (a) data-based, which uses statistics-based tools for analyzing patterns in the data or compute parameters, and (b) model-based, where the method considers both environmental and damagebased changes of stiffness value. This study uses a model-based approach to address the problem of detecting damage under different temperature levels in undamaged and damaged states. The proposed method uses an Approximate Bayesian computation Nested Sampling (ABC-NS) algorithm to detect damage under temperature variability. The study introduces a new damage index for identifying potentially damaged members. After performing damage localization, we estimate the parameters’ posterior distribution for potentially damaged members using ABC-NS. The estimated parameters’ mean value corresponds to the parameters’ actual values in the damaged state. In this study, we will see how to incorporate the effect of temperature variation and noise using a finite element model. One of the major assumptions in a lot of studies is that the structure remains in an equivalently linear regime accounting for damage. However, a breathing crack can lead to bi-linear stiffness and affect structural health monitoring strategies, classified as damage-induced nonlinearity. This study also incorporates damage-induced nonlinearity while performing damage detection.