Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms
The objective of this study is to develop data-driven predictive models for peak rotation and factor of safety for tipping-over failure of rocking shallow foundations during earthquake loading using multiple nonlinear machine learning (ML) algorithms and a supervised learning technique. Centrifuge a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Geotechnics |
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| Online Access: | https://www.mdpi.com/2673-7094/2/3/38 |
| _version_ | 1797487887286009856 |
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| author | Sivapalan Gajan |
| author_facet | Sivapalan Gajan |
| author_sort | Sivapalan Gajan |
| collection | DOAJ |
| description | The objective of this study is to develop data-driven predictive models for peak rotation and factor of safety for tipping-over failure of rocking shallow foundations during earthquake loading using multiple nonlinear machine learning (ML) algorithms and a supervised learning technique. Centrifuge and shaking table experimental results on rocking foundations have been used for the development of k-nearest neighbors regression (KNN), support vector regression (SVR), and random forest regression (RFR) models. The input features to ML models include critical contact area ratio of foundation; slenderness ratio and rocking coefficient of rocking system; peak ground acceleration and Arias intensity of earthquake motion; and a categorical binary feature that separates sandy soil foundations from clayey soil foundations. Based on repeated k-fold cross validation tests of models, we found that the overall average mean absolute percentage errors (MAPE) in predictions of all three nonlinear ML models varied between 0.46 and 0.60, outperforming a baseline multivariate linear regression ML model with corresponding MAPE of 0.68 to 0.75. The input feature importance analysis reveals that the peak rotation and tipping-over stability of rocking foundations are more sensitive to ground motion demand parameters than to rocking foundation capacity parameters or type of soil. |
| first_indexed | 2024-03-09T23:54:15Z |
| format | Article |
| id | doaj.art-1c57647b05e94e8fbd65201a019c0baf |
| institution | Directory Open Access Journal |
| issn | 2673-7094 |
| language | English |
| last_indexed | 2024-03-09T23:54:15Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geotechnics |
| spelling | doaj.art-1c57647b05e94e8fbd65201a019c0baf2023-11-23T16:27:31ZengMDPI AGGeotechnics2673-70942022-09-012378180110.3390/geotechnics2030038Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning AlgorithmsSivapalan Gajan0College of Engineering, SUNY Polytechnic Institute, Utica, NY 13502, USAThe objective of this study is to develop data-driven predictive models for peak rotation and factor of safety for tipping-over failure of rocking shallow foundations during earthquake loading using multiple nonlinear machine learning (ML) algorithms and a supervised learning technique. Centrifuge and shaking table experimental results on rocking foundations have been used for the development of k-nearest neighbors regression (KNN), support vector regression (SVR), and random forest regression (RFR) models. The input features to ML models include critical contact area ratio of foundation; slenderness ratio and rocking coefficient of rocking system; peak ground acceleration and Arias intensity of earthquake motion; and a categorical binary feature that separates sandy soil foundations from clayey soil foundations. Based on repeated k-fold cross validation tests of models, we found that the overall average mean absolute percentage errors (MAPE) in predictions of all three nonlinear ML models varied between 0.46 and 0.60, outperforming a baseline multivariate linear regression ML model with corresponding MAPE of 0.68 to 0.75. The input feature importance analysis reveals that the peak rotation and tipping-over stability of rocking foundations are more sensitive to ground motion demand parameters than to rocking foundation capacity parameters or type of soil.https://www.mdpi.com/2673-7094/2/3/38rocking foundationsearthquake engineeringsoil-structure interactiontipping-over stabilitymachine learning |
| spellingShingle | Sivapalan Gajan Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms Geotechnics rocking foundations earthquake engineering soil-structure interaction tipping-over stability machine learning |
| title | Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms |
| title_full | Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms |
| title_fullStr | Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms |
| title_full_unstemmed | Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms |
| title_short | Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms |
| title_sort | data driven modeling of peak rotation and tipping over stability of rocking shallow foundations using machine learning algorithms |
| topic | rocking foundations earthquake engineering soil-structure interaction tipping-over stability machine learning |
| url | https://www.mdpi.com/2673-7094/2/3/38 |
| work_keys_str_mv | AT sivapalangajan datadrivenmodelingofpeakrotationandtippingoverstabilityofrockingshallowfoundationsusingmachinelearningalgorithms |