Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms
Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and suc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/8/3845 |
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| author | Petros C. Lazaridis Ioannis E. Kavvadias Konstantinos Demertzis Lazaros Iliadis Lazaros K. Vasiliadis |
| author_facet | Petros C. Lazaridis Ioannis E. Kavvadias Konstantinos Demertzis Lazaros Iliadis Lazaros K. Vasiliadis |
| author_sort | Petros C. Lazaridis |
| collection | DOAJ |
| description | Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application. |
| first_indexed | 2024-03-09T11:13:01Z |
| format | Article |
| id | doaj.art-79e4c753c80a42d08f54faf4e16f83be |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:13:01Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-79e4c753c80a42d08f54faf4e16f83be2023-12-01T00:39:59ZengMDPI AGApplied Sciences2076-34172022-04-01128384510.3390/app12083845Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning AlgorithmsPetros C. Lazaridis0Ioannis E. Kavvadias1Konstantinos Demertzis2Lazaros Iliadis3Lazaros K. Vasiliadis4Department of Civil Engineering, Democritus University of Thrace, Campus of Kimmeria, 67100 Xanthi, GreeceDepartment of Civil Engineering, Democritus University of Thrace, Campus of Kimmeria, 67100 Xanthi, GreeceDepartment of Civil Engineering, Democritus University of Thrace, Campus of Kimmeria, 67100 Xanthi, GreeceDepartment of Civil Engineering, Democritus University of Thrace, Campus of Kimmeria, 67100 Xanthi, GreeceDepartment of Civil Engineering, Democritus University of Thrace, Campus of Kimmeria, 67100 Xanthi, GreeceAdvanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application.https://www.mdpi.com/2076-3417/12/8/3845seismic sequencemachine learning algorithmsrepeated earthquakesstructural damage predictionintensity measuresdamage accumulation |
| spellingShingle | Petros C. Lazaridis Ioannis E. Kavvadias Konstantinos Demertzis Lazaros Iliadis Lazaros K. Vasiliadis Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms Applied Sciences seismic sequence machine learning algorithms repeated earthquakes structural damage prediction intensity measures damage accumulation |
| title | Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms |
| title_full | Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms |
| title_fullStr | Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms |
| title_full_unstemmed | Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms |
| title_short | Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms |
| title_sort | structural damage prediction of a reinforced concrete frame under single and multiple seismic events using machine learning algorithms |
| topic | seismic sequence machine learning algorithms repeated earthquakes structural damage prediction intensity measures damage accumulation |
| url | https://www.mdpi.com/2076-3417/12/8/3845 |
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