Mohammad Sadegh Barkhordari1, Danial Jahed Armaghani2,*, Panagiotis G. Asteris3
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 835-855, 2023, DOI:10.32604/cmes.2022.020840
Abstract The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by
traditional visual methods, 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
hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification
is carried out on three types of damage. The proposed algorithms are… More >
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