A Caltech Library Service

A Bayesian Learning Method for Structural Damage Assessment of Phase I IASC-ASCE Benchmark Problem

Oh, Chang Kook and Beck, James L. (2018) A Bayesian Learning Method for Structural Damage Assessment of Phase I IASC-ASCE Benchmark Problem. KSCE Journal of Civil Engineering, 22 (3). pp. 987-992. ISSN 1226-7988. doi:10.1007/s12205-018-1290-1.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


Rapid progress in the field of sensor technology leads to acquisition of massive amounts of measured data from structures being monitored. The data, however, contains inevitable measurement errors which often cause quantitative damage assessment to be ill-conditioned. The Bayesian learning method is well known to provide effective ways to alleviate the ill-conditioning through the prior term for regularization and to provide meaningful probabilistic results for reliable decision-making at the same time. In this study, the Bayesian learning method, based on the Bayesian regression approach using the automatic relevance determination prior, is presented to achieve more effective regularization as well as probabilistic prediction and it is expanded to provide vector outputs for monitoring of a Phase I IASC-ASCE simulated benchmark problem. The proposed method successfully estimates damage locations as well as its severities and give considerable promise for structural damage assessment.

Item Type:Article
Related URLs:
URLURL TypeDescription ReadCube access
Additional Information:ⓒ 2018 Korean Society of Civil Engineers and Springer-Verlag. Received September 15, 2017. Revised 1st: December 8, 2017. Accepted December 15, 2017.
Subject Keywords:Bayesian learning method vector outputs automatic relevance determination prior damage assessment Phase I IASCASCE benchmark problem
Issue or Number:3
Record Number:CaltechAUTHORS:20180312-114520324
Persistent URL:
Official Citation:Oh, C.K. & Beck, J.L. KSCE J Civ Eng (2018) 22: 987.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:85246
Deposited By: Ruth Sustaita
Deposited On:12 Mar 2018 20:07
Last Modified:15 Nov 2021 20:27

Repository Staff Only: item control page