@inproceedings{f8c894d282cc45ec8a5743ba03ddad49,
title = "Structural damage identification using piezoelectric impedance and Bayesian inference",
abstract = "Structural damage identification is a challenging subject in the structural health monitoring research. The piezoelectric impedance-based damage identification, which usually utilizes the matrix inverse-based optimization, may in theory identify the damage location and damage severity. However, the sensitivity matrix is oftentimes ill-conditioned in practice, since the number of unknowns may far exceed the useful measurements/inputs. In this research, a new method based on intelligent inference framework for damage identification is presented. Bayesian inference is used to directly predict damage location and severity using impedance measurement through forward prediction and comparison. Gaussian process is employed to enrich the forward analysis result, thereby reducing computational cost. Case study is carried out to illustrate the identification performance.",
keywords = "Bayesian inference, Damage identification, Gaussian process, Impedance",
author = "Q. Shuai and K. Zhou and J. Tang",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015 ; Conference date: 09-03-2015 Through 12-03-2015",
year = "2015",
doi = "10.1117/12.2084442",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Hoon Sohn and Kon-Well Wang and Lynch, {Jerome P.}",
booktitle = "Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015",
address = "United States",
}