Modeling Remaining Service Life and Structural Health Monitoring of Roads with Machine Learning and Deep Learning

Mudabir M. Mosavi A. Imre F. Moniz N. Iskakov K. Mohamadreza A.
2025 Institute of Electrical and Electronics Engineers Inc.

Proceedings of the International Symposium on Applied Machine Intelligence and Informatics, SAMI
2025 413 - 422 pp.

The integration of machine learning (ML) and deep learning (DL) in structural health monitoring (SHM) and remaining service life (RSL) has revolutionized the ability to assess and maintain critical infrastructure. This review looks at the current state of SHM methods that use ML and DL. This is done by providing a detailed taxonomy that groups these methods into groups based on algorithmic strategies, data sources, and specific SHM and RSL applications. Using Scopus as the primary source for literature, we conducted a systematic review following PRISMA guidelines to ensure thorough screening and quality assessment of most relevant studies. The review covers key areas that include supervised and unsupervised learning techniques, neural networks, and their applications to structural damage detection, failure prediction, improving precision in monitoring. Based on the trend analysis and highlighting of some of the challenges in this context, this review has identified a few future opportunities for applying advanced learning techniques to SHM to improve infrastructure safety and management.

advanced analytics , applied informatics , applied mathematics , automated damage detection , big data , civil engineering , computational mechanics , condition-based monitoring , data science , data-driven engineering , deep learning , digital infrastructure , generating AI , hybrid learning models , informatics , information sciences , information systems , infrastructure durability , infrastructure monitoring , infrastructures , intelligent systems , machine learning , machine vision , mathematical model , mathematical modeling , mathematics , predictive modeling , remaining service life , sensor networks , smart cities , structural assessment , structural engineering , structural health monitoring , survey , systems engineering , time-series forecasting , unsupervised anomaly detection , XAI

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John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
Ludovika University of Public Service, Budapest, Hungary
Instituto Superior Técnico, Lisbon, Lisbon, Portugal
L.N. Gumilyov Eurasian National University, Astana, Kazakhstan

John von Neumann Faculty of Informatics
Ludovika University of Public Service
Instituto Superior Técnico
L.N. Gumilyov Eurasian National University

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026