# Structural Damage Detection and Localization with Unknown Post-Damage Feature Distribution Using Sequential Change-Point Detection Method

## TL;DR

This paper introduces a novel damage detection and localization method for structures using sequential change-point detection on vibration data, effectively handling unknown post-damage feature distributions without prior damage information.

## Contribution

It develops a new approach combining stochastic time series analysis and maximum likelihood estimation to detect damage and locate it accurately without needing post-damage data.

## Key findings

- High accuracy in damage detection and localization
- Validated on benchmark structures and shake table experiments
- Effective even with unknown post-damage feature distributions

## Abstract

The high structural deficient rate poses serious risks to the operation of many bridges and buildings. To prevent critical damage and structural collapse, a quick structural health diagnosis tool is needed during normal operation or immediately after extreme events. In structural health monitoring (SHM), many existing works will have limited performance in the quick damage identification process because 1) the damage event needs to be identified with short delay and 2) the post-damage information is usually unavailable. To address these drawbacks, we propose a new damage detection and localization approach based on stochastic time series analysis. Specifically, the damage sensitive features are extracted from vibration signals and follow different distributions before and after a damage event. Hence, we use the optimal change point detection theory to find damage occurrence time. As the existing change point detectors require the post-damage feature distribution, which is unavailable in SHM, we propose a maximum likelihood method to learn the distribution parameters from the time-series data. The proposed damage detection using estimated parameters also achieves the optimal performance. Also, we utilize the detection results to find damage location without any further computation. Validation results show highly accurate damage identification in American Society of Civil Engineers benchmark structure and two shake table experiments.