A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data
This research paper presents a novel structural health monitoring strategy based on a hybrid machine learning and finite element model updating method for the health monitoring of bolted connections in steel planer frame structures using vibration data. Towards this, a support vector machine model i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/11107 |
| _version_ | 1797469219195977728 |
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| author | Joy Pal Shirsendu Sikdar Sauvik Banerjee Pradipta Banerji |
| author_facet | Joy Pal Shirsendu Sikdar Sauvik Banerjee Pradipta Banerji |
| author_sort | Joy Pal |
| collection | DOAJ |
| description | This research paper presents a novel structural health monitoring strategy based on a hybrid machine learning and finite element model updating method for the health monitoring of bolted connections in steel planer frame structures using vibration data. Towards this, a support vector machine model is trained with the discriminative features obtained from time history data, and those features are used to distinguish between damaged and undamaged joints. An FE model of the planer frame is considered where the fixity factor (FF) of a joint is modeled with rational springs and the FF of the spring is assumed as the severity level of loosening bolts. The Cat Swarm Optimization technique is further applied to update the FE model to calculate the fixity factors of damaged joints. Initially, the method is applied to a laboratory-based experimental model of a single-story planer frame structure and later extended to a pseudo-numerical four-story planer frame structure. The results show that the method successfully localizes the damaged joints and estimates their fixity factors. |
| first_indexed | 2024-03-09T19:17:16Z |
| format | Article |
| id | doaj.art-8d29bd3804cf479db9059c3fd02cd9c3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:17:16Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8d29bd3804cf479db9059c3fd02cd9c32023-11-24T03:38:07ZengMDPI AGApplied Sciences2076-34172022-11-0112211110710.3390/app122111107A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration DataJoy Pal0Shirsendu Sikdar1Sauvik Banerjee2Pradipta Banerji3Department of Civil Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, IndiaCardiff School of Engineering, Cardiff University, The Parade, Queen’s Building, Cardiff CF24 3AA, UKDepartment of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, IndiaDepartment of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, IndiaThis research paper presents a novel structural health monitoring strategy based on a hybrid machine learning and finite element model updating method for the health monitoring of bolted connections in steel planer frame structures using vibration data. Towards this, a support vector machine model is trained with the discriminative features obtained from time history data, and those features are used to distinguish between damaged and undamaged joints. An FE model of the planer frame is considered where the fixity factor (FF) of a joint is modeled with rational springs and the FF of the spring is assumed as the severity level of loosening bolts. The Cat Swarm Optimization technique is further applied to update the FE model to calculate the fixity factors of damaged joints. Initially, the method is applied to a laboratory-based experimental model of a single-story planer frame structure and later extended to a pseudo-numerical four-story planer frame structure. The results show that the method successfully localizes the damaged joints and estimates their fixity factors.https://www.mdpi.com/2076-3417/12/21/11107structural health monitoringmachine learningmodel updatingsteel frameloosening of boltsCat Swarm Optimization |
| spellingShingle | Joy Pal Shirsendu Sikdar Sauvik Banerjee Pradipta Banerji A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data Applied Sciences structural health monitoring machine learning model updating steel frame loosening of bolts Cat Swarm Optimization |
| title | A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data |
| title_full | A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data |
| title_fullStr | A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data |
| title_full_unstemmed | A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data |
| title_short | A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data |
| title_sort | combined machine learning and model updating method for autonomous monitoring of bolted connections in steel frame structures using vibration data |
| topic | structural health monitoring machine learning model updating steel frame loosening of bolts Cat Swarm Optimization |
| url | https://www.mdpi.com/2076-3417/12/21/11107 |
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