Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring
Structural monitoring is a research topic that is receiving more and more attention, especially in light of the fact that a large part our infrastructural heritage was built in the Sixties and is aging and approaching the end of its design working life. The detection of damage is usually performed t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/20/7157 |
| _version_ | 1797550974158503936 |
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| author | Bernardino Chiaia Valerio De Biagi |
| author_facet | Bernardino Chiaia Valerio De Biagi |
| author_sort | Bernardino Chiaia |
| collection | DOAJ |
| description | Structural monitoring is a research topic that is receiving more and more attention, especially in light of the fact that a large part our infrastructural heritage was built in the Sixties and is aging and approaching the end of its design working life. The detection of damage is usually performed through artificial intelligence techniques. In contrast, tools for the localization and the estimation of the extent of the damage are limited, mainly due to the complete datasets of damages needed for training the system. The proposed approach consists in numerically generating datasets of damaged structures on the basis of random variables representing the actions and the possible damages. Neural networks were trained to perform the main structural monitoring tasks: damage detection, localization, and estimation. The artificial intelligence tool interpreted the measurements on a real structure. To simulate real measurements more accurately, noise was added to the synthetic dataset. The results indicate that the accuracy of the measurement devices plays a relevant role in the quality of the monitoring. |
| first_indexed | 2024-03-10T15:38:05Z |
| format | Article |
| id | doaj.art-05e42a50bba24bf98c65baef71089373 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:38:05Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-05e42a50bba24bf98c65baef710893732023-11-20T17:04:00ZengMDPI AGApplied Sciences2076-34172020-10-011020715710.3390/app10207157Archetypal Use of Artificial Intelligence for Bridge Structural MonitoringBernardino Chiaia0Valerio De Biagi1DISEG, Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Torino, ItalyDISEG, Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Torino, ItalyStructural monitoring is a research topic that is receiving more and more attention, especially in light of the fact that a large part our infrastructural heritage was built in the Sixties and is aging and approaching the end of its design working life. The detection of damage is usually performed through artificial intelligence techniques. In contrast, tools for the localization and the estimation of the extent of the damage are limited, mainly due to the complete datasets of damages needed for training the system. The proposed approach consists in numerically generating datasets of damaged structures on the basis of random variables representing the actions and the possible damages. Neural networks were trained to perform the main structural monitoring tasks: damage detection, localization, and estimation. The artificial intelligence tool interpreted the measurements on a real structure. To simulate real measurements more accurately, noise was added to the synthetic dataset. The results indicate that the accuracy of the measurement devices plays a relevant role in the quality of the monitoring.https://www.mdpi.com/2076-3417/10/20/7157structural health monitoringdamage detectiondamage localizationhybrid approachneural network |
| spellingShingle | Bernardino Chiaia Valerio De Biagi Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring Applied Sciences structural health monitoring damage detection damage localization hybrid approach neural network |
| title | Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring |
| title_full | Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring |
| title_fullStr | Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring |
| title_full_unstemmed | Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring |
| title_short | Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring |
| title_sort | archetypal use of artificial intelligence for bridge structural monitoring |
| topic | structural health monitoring damage detection damage localization hybrid approach neural network |
| url | https://www.mdpi.com/2076-3417/10/20/7157 |
| work_keys_str_mv | AT bernardinochiaia archetypaluseofartificialintelligenceforbridgestructuralmonitoring AT valeriodebiagi archetypaluseofartificialintelligenceforbridgestructuralmonitoring |