Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

Mohammad Sadegh Barkhordari; Danial Jahed Armaghani; Panagiotis G.Asteris

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Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

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The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise.As a result,a robust,reliable,and repeatable method of damage identification is required.Ensemble learning algorithms for identifying structural damage are evaluated in this article,which use deep convolutional neural networks,including simple averaging,integrated stacking,separate stacking,and hybridweighted averaging ensemble and differential evolution(WAE-DE)ensemblemodels.Damage identification is carried out on three types of damage.The proposed algorithms are used to analyze the damage of 4585 structural images.The effectiveness of the ensemble learning techniques is evaluated using the confusion matrix.For the testing dataset,the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model(WAE-DE)in distinguishing damage types as flexural,shear,combined,or undamaged.

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来源
《工程与科学中的计算机建模（英文）》 |2023年第2期|835-855|共21页
作者
Mohammad Sadegh Barkhordari; Danial Jahed Armaghani; Panagiotis G.Asteris;
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作者单位

Department of Civil and Environmental Engineering University of Technology;

Department of Urban Planning Networks and Systems of Architecture and Construction Ural State University;

Department of Computational Mechanics Laboratory of Pedagogical and Technological Education;

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原文格式 PDF
正文语种 chi
中图分类自动化基础理论;
关键词
Machine learning; ensemble learning algorithms; convolutional neural network; damage assessment; structural damage;

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Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

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