A probability distribution-based approach to damage detection: An investigation on a defective Euler-Bernoulli beam

Abstract

Structural health monitoring (SHM). is an important tool to assess a structure's integrity level. One way to monitor a structure's serviceability is by detecting the location of the damage caused in it. In present work, we use a probability distribution-based approach to detect damage in a defective Euler-Bernoulli (EB) beam. The Ensemble Kushner-Stratonovich (EnKS) NonistributionLinear Filter is adopted to detect the damage. We evaluate the same by posing an inverse problem and detecting the damage (in terms of normalized flexural rigidity) by exploiting the true process data (displacement field) obtained from simulations on a EB beam subjected to external static loading. © 2022 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.