Damage Detection in Structures by Using Imbalanced Classification Algorithms

Kasra Yousefi Moghadam, Mohammad Noori (), Ahmed Silik and Wael A. Altabey ()
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Kasra Yousefi Moghadam: Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 009821, Iran
Mohammad Noori: Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
Ahmed Silik: International Institute of Urban System Engineering (IIUSE), Southeast University, Nanjing 211189, China
Wael A. Altabey: International Institute of Urban System Engineering (IIUSE), Southeast University, Nanjing 211189, China

Mathematics, 2024, vol. 12, issue 3, 1-29

Abstract: Detecting damage constitutes the primary and pivotal stage in monitoring a structure’s health. Early identification of structural issues, coupled with a precise understanding of the structure’s condition, represents a cornerstone in the practices of structural health monitoring (SHM). While many existing methods prove effective when the number of data points in both healthy and damaged states is equal, this article employs algorithms tailored for detecting damage in situations where data are imbalanced. Imbalance, in this context, denotes a significant difference in the number of data points between the healthy and damaged states, essentially introducing an imbalance within the dataset. Four imbalanced classification algorithms are applied to two benchmark structures: the first, a numerical model of a four-story steel building, and the second, a bridge constructed in China. This research thoroughly assesses the performance of these four algorithms for each structure, both individually and collectively.

Keywords: structural health monitoring (SHM); damage detection; imbalanced data classification; artificial intelligence; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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