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

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.

[1]  Michael E. Tipping.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[2]  James L. Beck,et al.  Bayesian Analysis of the Phase II IASC–ASCE Structural Health Monitoring Experimental Benchmark Data , 2004 .

[3]  James L. Beck,et al.  Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural Networks , 2006, Comput. Aided Civ. Infrastructure Eng..

[4]  James L. Beck,et al.  System Identification Methods Applied to Measured Seismic Response , 1996 .

[5]  Michael E. Tipping.  The Relevance Vector Machine , 1999, NIPS.

[6]  Hoon Sohn,et al.  Statistical novelty detection within the Yeongjong suspension bridge under environmental and operational variations , 2009 .

[7]  James L. Beck,et al.  Bayesian Learning Using Automatic Relevance Determination Prior with an Application to Earthquake Early Warning , 2008 .

[8]  Hoon Sohn,et al.  Damage diagnosis under environmental and operational variations using unsupervised support vector machine , 2009 .

[9]  Ka-Veng Yuen,et al.  On the complexity of artificial neural networks for smart structures monitoring , 2006 .

[10]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[11]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[12]  Lambros S. Katafygiotis,et al.  A BENCHMARK PROBLEM FOR STRUCTURAL HEALTH MONITORING AND DAMAGE DETECTION , 2000 .

[13]  Ahmed Y. Elghazouli,et al.  Experimental cyclic response of cold-formed hollow steel bracing members , 2005 .

[14]  Michael E. Tipping,et al.  Fast Marginal Likelihood Maximisation for Sparse Bayesian Models , 2003 .

[15]  J. Beck.  Bayesian system identification based on probability logic , 2010 .

[16]  Chang Kook Oh,et al.  Bayesian Learning for Earthquake EngineeringApplications and Structural Health Monitoring , 2007 .

[17]  Achintya Haldar,et al.  Probability, Reliability and Statistical Methods in Engineering Design (Haldar, Mahadevan) , 1999 .

[18]  James L. Beck,et al.  Two-Stage Structural Health Monitoring Approach for Phase I Benchmark Studies , 2004 .

[19]  J. Beck,et al.  Model Selection using Response Measurements: Bayesian Probabilistic Approach , 2004 .

[20]  Erik A. Johnson,et al.  Phase I IASC-ASCE Structural Health Monitoring Benchmark Problem Using Simulated Data , 2004 .