Comparative study of AI-enabled damage detection strategies based on story drifts and stiffness reductions for seismically-excited building
Journal
Proceedings of SPIE - The International Society for Optical Engineering
Journal Volume
12951
ISBN (of the container)
9781510672086
ISBN
9781510672086
Date Issued
2024-05-09
Author(s)
Chieh Yu Liu
DOI
10.1117/12.3010151
Abstract
Damage detection plays a pivotal role in structural health monitoring. As indicated in FEMA and ASCE, structural damage relies on story drifts as a fundamental criterion for categorizing damage states and assessing risk levels. In addition, past studies showed that structural stiffness changes were closely linked to the extent of structural damage due to earthquakes. However, both story drifts and stiffness changes are rarely evaluated concurrently to determine structural damage. In this study, three multi-target neural networks are developed using floor accelerations of buildings under seismic excitation to estimate story drifts and remaining stiffness ratios. Notably, all network architectures are identical. The three neural networks in this study differ in applying distinct loss functions and training strategies to assess and compare the performance of the models. All networks are compared through numerical investigation using a three-story finite element model and experimentally verified using a seismically-excited full-scale building.
Event(s)
Health Monitoring of Structural and Biological Systems XVIII 2024, Long Beach, 25 March 2024 through 28 March 2024, Code 199777
Subjects
Damage detection
loss-conditional training
multi-target neural network
physics-guided loss function
remaining stiffness ratio
story drift estimation
SDGs
Publisher
SPIE
Description
Article number 129511K
Type
conference paper