An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group method of data
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RESEARCH ARTICLE
An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate model Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN* Department of Civil Engineering, Yazd University, Yazd 89195-741, Iran Corresponding author. E-mail: [email protected]
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© Higher Education Press 2020
ABSTRACT In this study, the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated. In the first stage, in order to locate the damage accurately, the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix. In the second stage, to estimate the damage extent, the sensitivity of most used modal properties due to damage, such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy (MNMSE) of suspected damaged elements. Moreover, a modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogate model during damage extent estimation by optimization algorithm; in this part of methodology, the performance of the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), and colliding bodies optimization (CBO) is examined and in this regard, root mean square deviation (RMSD) based on the modal property change vector has been proposed as an objective function. Furthermore, the effect of noise in the measurement of structural responses by the sensors has also been studied. Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance is compared with a properly trained cascade feed-forward neural network (CFNN) with log-sigmoid hidden layer transfer function. The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE. Moreover, the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model. KEYWORDS
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two-stage method, modal strain energy, surrogate model, GMDH, optimization damage detection
Introduction
Health monitoring of structures, involves monitoring and analyzing the static and dynamic behavior of structures, has become a topic of great importance in recent years. Aging and damage in different types of structures, such as dams, bridges, and buildings are becoming an important issue nowadays [1–3]. Long-term monitoring of these structures, particularly large-scale ones, requires dealing with high dimensional data sets, captured from different types of sensors [4]. These raw data sets must be effectively processed and interpreted. There are two distinct approaches for an interpretation of SHM data, Article history: Received May 2019; Accepted Jul 5, 2019
so-called model-based and model-free [5]. In the modelbased approaches, development of the large-scale structural model is time
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