Machine-Learning Applications in Structural Response Prediction: A Review
Publication: Practice Periodical on Structural Design and Construction
Volume 29, Issue 3
Abstract
Structural health monitoring (SHM) is an important and practical procedure for ensuring the structural integrity and serviceability of civil engineering structures such as bridges, buildings, and dams. Model-driven or data-driven strategies for structural response prediction are now widely combined with advances in engineering for use in SHM applications. Engineers have recently demonstrated increasing interest in using machine learning (ML) and artificial intelligence (AI) to achieve a variety of benefits and possibilities, notably for predicting structural reactions. This study serves as a comprehensive overview of the use of ML applications for structural response prediction in the context of SHM for civil engineering structures, with a particular focus on ML, deep learning (DL), and meta-heuristic algorithms. Accordingly, this study summarizes existing knowledge, presents concepts in a simple way, highlights trends, provides methodological insights, and provides a valuable resource for researchers, stakeholders, and decision-makers to benefit from. It is observed that the use of ML, DL, and meta-heuristic algorithms to predict the response of civil engineering structures within an acceptable accuracy range can be employed for SHM, resulting in improved speed, efficiency, and accuracy compared to conventional approaches.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
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Practice Periodical on Structural Design and Construction
Volume 29 • Issue 3 • August 2024
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© 2024 American Society of Civil Engineers.
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Published online: Mar 18, 2024
Published in print: Aug 1, 2024
Discussion open until: Aug 18, 2024
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