Probabilistic Multi-Objective Inverse Analysis for Damage Identification Using Piezoelectric Impedance Measurement Under Uncertainties
Probabilistic Multi-Objective Inverse Analysis for Damage Identification Using Piezoelectric Impedance Measurement Under Uncertainties
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DOI：
10.3389/fbuil.2022.904690
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
K. Zhou;Yang Zhang;Q. Shuai;Jiong Tang
中科院分区：
文献类型：
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作者：
K. Zhou;Yang Zhang;Q. Shuai;Jiong Tang
压电阻抗传感由于其高频主动询问的性质和简单的数据采集，是非常有前途的高精度损伤识别。为了充分发挥潜力，需要进行有效的反分析，以确定损伤位置并确定严重程度。然而，由于存在非常大量的未知数（即，位置和严重程度）在有限元模型中求解，但在实际应用中只有有限的测量值可用。为了揭示真实的损伤情况，建立在多目标优化的逆分析策略，其目的是在匹配的多组测量与模型预测的损伤参数空间，可以制定识别一个小的解决方案。这个解决方案集，然后允许纳入经验知识，以促进最终决策。传统的逆分析策略的主要缺点是它忽略了基线结构建模和实际测量中存在的不确定性。为了解决这个问题，在这项研究中，我们制定了一个概率多目标优化为基础的逆分析框架，这是从根本上建立在差分进化马尔可夫链蒙特卡罗（DEMC）技术。新的方法可以产生帕累托最优集（解决方案）和相应的帕累托前沿，这是在概率意义上表示的不确定性。综合的案例研究与实验调查进行证明这种新方法的有效性。
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.