Structural Damage Detection in Civil Engineering with Machine Learning: Current State of the Art
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| Název: | Structural Damage Detection in Civil Engineering with Machine Learning: Current State of the Art |
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| Autoři: | Avci O., Abdeljaber O., Kiranyaz, Mustafa Serkan |
| Zdroj: | Sensors and Instrumentation, Aircraft/Aerospace, Energy Harvesting & Dynamic Environments Testing, Volume 7 ISBN: 9788743803850 |
| Informace o vydavateli: | River Publishers, 2025. |
| Rok vydání: | 2025 |
| Témata: | Structural damage detection, Structural health monitoring, Damage localization, 02 engineering and technology, Civil engineering structures, Damage detection, Computer programming, State of the art, Nonparametrics, 0201 civil engineering, 'current, Learn+, Vibration-based method, Machine learning, Damage Identification, 0202 electrical engineering, electronic engineering, information engineering, Structural dynamics, Structures (built objects), Machine-learning |
| Popis: | This paper presents a brief overview of vibration-based structural damage detection studies that are based on machine learning (ML) in civil engineering structures. The review includes both parametric and nonparametric applications of ML accompanied with analytical and/or experimental studies. While the ML tools help the system learn from the data fed into, the computer enhances the task with the learned information without any programming on how to process the relevant data. As such, the performance level of ML-based damage identification methodologies depends on the feature extraction and classification steps, especially on the classifier choices for which the characteristic nature of the acceleration signals is recorded in a feasible way. Yet, there are several issues to be discussed about the existing ML procedures for both parametric and nonparametric applications, which are presented in this paper. |
| Druh dokumentu: | Part of book or chapter of book Other literature type Conference object |
| Jazyk: | English |
| DOI: | 10.1007/978-3-030-75988-9_17 |
| Přístupová URL adresa: | https://link.springer.com/chapter/10.1007/978-3-030-75988-9_17 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118193312&doi=10.1007/978-3-030-75988-9_17&partnerID=40&md5=54b7815adfb1a14bf6d0473b14249d2f https://hdl.handle.net/10576/30582 |
| Rights: | Springer TDM |
| Přístupové číslo: | edsair.doi.dedup.....e0f8a665b9083e8c51c541ac13e344ab |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | This paper presents a brief overview of vibration-based structural damage detection studies that are based on machine learning (ML) in civil engineering structures. The review includes both parametric and nonparametric applications of ML accompanied with analytical and/or experimental studies. While the ML tools help the system learn from the data fed into, the computer enhances the task with the learned information without any programming on how to process the relevant data. As such, the performance level of ML-based damage identification methodologies depends on the feature extraction and classification steps, especially on the classifier choices for which the characteristic nature of the acceleration signals is recorded in a feasible way. Yet, there are several issues to be discussed about the existing ML procedures for both parametric and nonparametric applications, which are presented in this paper. |
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| DOI: | 10.1007/978-3-030-75988-9_17 |