@Article{cmes.2022.020840,
AUTHOR = {Mohammad Sadegh Barkhordari, Danial Jahed Armaghani, Panagiotis G. Asteris},
TITLE = {Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {2},
PAGES = {835--855},
URL = {http://www.techscience.com/CMES/v134n2/49512},
ISSN = {1526-1506},
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 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.},
DOI = {10.32604/cmes.2022.020840}
}