Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage
Abstract Structural health monitoring ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need for SHM techniques that are scalable, inexpensive, can operate passively witho...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-52501-4 |
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| author | Xuyang Li Hamed Bolandi Mahdi Masmoudi Talal Salem Ankush Jha Nizar Lajnef Vishnu Naresh Boddeti |
| author_facet | Xuyang Li Hamed Bolandi Mahdi Masmoudi Talal Salem Ankush Jha Nizar Lajnef Vishnu Naresh Boddeti |
| author_sort | Xuyang Li |
| collection | DOAJ |
| description | Abstract Structural health monitoring ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need for SHM techniques that are scalable, inexpensive, can operate passively without human intervention, and are customized for each mechanical structure without the need for complex baseline models. We present a novel “deploy-and-forget” approach for automated detection and localization of damage in structures. It is a synergistic integration of entirely passive measurements from inexpensive sensors, data compression, and a mechanics-informed autoencoder. Once deployed, the model continuously learns and adapts a bespoke baseline model for each structure, learning from its undamaged state’s response characteristics. After learning from just 3 hours of data, it can autonomously detect and localize different types of unforeseen damage. Results from numerical simulations and experiments indicate that incorporating the mechanical characteristics into the autoencoder allows for up to a 35% improvement in the detection and localization of minor damage over a standard autoencoder. Our approach holds significant promise for reducing human intervention and inspection costs while enabling proactive and preventive maintenance strategies. This will extend the lifespan, reliability, and sustainability of civil infrastructures. |
| format | Article |
| id | doaj-art-d062a002c8ac4efbb624a1e4a50600ef |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-d062a002c8ac4efbb624a1e4a50600ef2025-08-20T02:11:26ZengNature PortfolioNature Communications2041-17232024-10-0115111310.1038/s41467-024-52501-4Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damageXuyang Li0Hamed Bolandi1Mahdi Masmoudi2Talal Salem3Ankush Jha4Nizar Lajnef5Vishnu Naresh Boddeti6Michigan State UniversityMichigan State UniversityMichigan State UniversityMichigan State UniversityMichigan State UniversityMichigan State UniversityMichigan State UniversityAbstract Structural health monitoring ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need for SHM techniques that are scalable, inexpensive, can operate passively without human intervention, and are customized for each mechanical structure without the need for complex baseline models. We present a novel “deploy-and-forget” approach for automated detection and localization of damage in structures. It is a synergistic integration of entirely passive measurements from inexpensive sensors, data compression, and a mechanics-informed autoencoder. Once deployed, the model continuously learns and adapts a bespoke baseline model for each structure, learning from its undamaged state’s response characteristics. After learning from just 3 hours of data, it can autonomously detect and localize different types of unforeseen damage. Results from numerical simulations and experiments indicate that incorporating the mechanical characteristics into the autoencoder allows for up to a 35% improvement in the detection and localization of minor damage over a standard autoencoder. Our approach holds significant promise for reducing human intervention and inspection costs while enabling proactive and preventive maintenance strategies. This will extend the lifespan, reliability, and sustainability of civil infrastructures.https://doi.org/10.1038/s41467-024-52501-4 |
| spellingShingle | Xuyang Li Hamed Bolandi Mahdi Masmoudi Talal Salem Ankush Jha Nizar Lajnef Vishnu Naresh Boddeti Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage Nature Communications |
| title | Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage |
| title_full | Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage |
| title_fullStr | Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage |
| title_full_unstemmed | Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage |
| title_short | Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage |
| title_sort | mechanics informed autoencoder enables automated detection and localization of unforeseen structural damage |
| url | https://doi.org/10.1038/s41467-024-52501-4 |
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