TY - JOUR
T1 - Probabilistic Multi-Objective Inverse Analysis for Damage Identification Using Piezoelectric Impedance Measurement Under Uncertainties
AU - Zhou, Kai
AU - Zhang, Yang
AU - Shuai, Qi
AU - Tang, Jiong
N1 - Funding Information:
This research is supported in part by the National Science Foundation under grant CMMI—2138522 and in part by the National Science Foundation under grant CMMI—1825324.
Publisher Copyright:
Copyright © 2022 Zhou, Zhang, Shuai and Tang.
PY - 2022/6/14
Y1 - 2022/6/14
N2 - Piezoelectric impedance sensing is promising for highly accurate damage identification because of its high-frequency active interrogative nature and simplicity in data acquisition. To fully unleash the potential, effective inverse analysis is needed in order to pinpoint the damage location and identify the severity. The inverse analysis, however, may be underdetermined since there exists a very large number of unknowns (i.e., locations and severity levels) to be solved in a finite element model but only limited measurements are available in actual practice. To uncover the true damage scenario, an inverse analysis strategy built upon the multi-objective optimization, which aims at matching the multiple sets of measurements with model predictions in the damage parametric space, can be formulated to identify a small set of solutions. This solution set then allows the incorporation of empirical knowledge to facilitate final decision-making. The main disadvantage of the conventional inverse analysis strategy is that it overlooks uncertainties that exist in both baseline structural modeling and actual measurements. To address this, in this research, we formulate a probabilistic multi-objective optimization-based inverse analysis framework, which is fundamentally built upon the differential evolution Markov chain Monte Carlo (DEMC) technique. The new approach can yield the Pareto optimal set (solutions) and the respective Pareto front, which are represented in a probabilistic sense to account for uncertainties. Comprehensive case studies with experimental investigations are conducted to demonstrate the effectiveness of this new approach.
AB - Piezoelectric impedance sensing is promising for highly accurate damage identification because of its high-frequency active interrogative nature and simplicity in data acquisition. To fully unleash the potential, effective inverse analysis is needed in order to pinpoint the damage location and identify the severity. The inverse analysis, however, may be underdetermined since there exists a very large number of unknowns (i.e., locations and severity levels) to be solved in a finite element model but only limited measurements are available in actual practice. To uncover the true damage scenario, an inverse analysis strategy built upon the multi-objective optimization, which aims at matching the multiple sets of measurements with model predictions in the damage parametric space, can be formulated to identify a small set of solutions. This solution set then allows the incorporation of empirical knowledge to facilitate final decision-making. The main disadvantage of the conventional inverse analysis strategy is that it overlooks uncertainties that exist in both baseline structural modeling and actual measurements. To address this, in this research, we formulate a probabilistic multi-objective optimization-based inverse analysis framework, which is fundamentally built upon the differential evolution Markov chain Monte Carlo (DEMC) technique. The new approach can yield the Pareto optimal set (solutions) and the respective Pareto front, which are represented in a probabilistic sense to account for uncertainties. Comprehensive case studies with experimental investigations are conducted to demonstrate the effectiveness of this new approach.
KW - damage identification
KW - differential evolution Markov chain Monte Carlo (DEMC)
KW - inverse analysis
KW - piezoelectric impedance
KW - probabilistic multi-objective optimization
KW - uncertainties
UR - http://www.scopus.com/inward/record.url?scp=85133539983&partnerID=8YFLogxK
U2 - 10.3389/fbuil.2022.904690
DO - 10.3389/fbuil.2022.904690
M3 - Journal article
AN - SCOPUS:85133539983
SN - 2297-3362
VL - 8
JO - Frontiers in Built Environment
JF - Frontiers in Built Environment
M1 - 904690
ER -