Pushover-ML: A Machine Learning approach to predict a trilinear approximation of pushover curves for low-rise reinforced concrete frame buildings
The seismic design of low-rise RC building frames often relies on elastic procedures, limiting the evaluation of nonlinear behavior due to practical constraints such as computational cost. While the research community has applied Machine Learning (ML) to predict the seismic response, existing tools...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025000895 |
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| author | Carlos Angarita Carlos Montes Orlando Arroyo |
| author_facet | Carlos Angarita Carlos Montes Orlando Arroyo |
| author_sort | Carlos Angarita |
| collection | DOAJ |
| description | The seismic design of low-rise RC building frames often relies on elastic procedures, limiting the evaluation of nonlinear behavior due to practical constraints such as computational cost. While the research community has applied Machine Learning (ML) to predict the seismic response, existing tools often require prior knowledge and expertise to manage dependencies, configure programming environments, and execute code in languages such as Python. This paper introduces Pushover-ML, a graphical user interface (GUI) designed to efficiently predict a trilinear approximation of pushover curves for low-rise RC frames using an ML-based approach. The user-friendly executable provides insights into the structure's seismic capacity through the yielding, maximum capacity, and collapse points of the pushover curve. Pushover-ML bridges the gap between advanced ML techniques and practical engineering applications, enabling accurate and efficient seismic response predictions. |
| format | Article |
| id | doaj-art-8d6ec00e7a674e589f3fae723bf5eb9e |
| institution | Kabale University |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-8d6ec00e7a674e589f3fae723bf5eb9e2025-08-20T03:48:14ZengElsevierSoftwareX2352-71102025-05-013010212210.1016/j.softx.2025.102122Pushover-ML: A Machine Learning approach to predict a trilinear approximation of pushover curves for low-rise reinforced concrete frame buildingsCarlos Angarita0Carlos Montes1Orlando Arroyo2Energy, Materials and Environment Laboratory, Faculty of Engineering, Universidad de La Sabana, Chía, ColombiaEnergy, Materials and Environment Laboratory, Faculty of Engineering, Universidad de La Sabana, Chía, Colombia; Corresponding author at: Energy, Materials and Environment Laboratory, Faculty of Engineering, Universidad de La Sabana, 250001, Chía, Cundinamarca, Colombia.Universidad Industrial de Santander, Bucaramanga, Colombia; Colombian Earthquake Engineering Research Network, Bogotá, ColombiaThe seismic design of low-rise RC building frames often relies on elastic procedures, limiting the evaluation of nonlinear behavior due to practical constraints such as computational cost. While the research community has applied Machine Learning (ML) to predict the seismic response, existing tools often require prior knowledge and expertise to manage dependencies, configure programming environments, and execute code in languages such as Python. This paper introduces Pushover-ML, a graphical user interface (GUI) designed to efficiently predict a trilinear approximation of pushover curves for low-rise RC frames using an ML-based approach. The user-friendly executable provides insights into the structure's seismic capacity through the yielding, maximum capacity, and collapse points of the pushover curve. Pushover-ML bridges the gap between advanced ML techniques and practical engineering applications, enabling accurate and efficient seismic response predictions.http://www.sciencedirect.com/science/article/pii/S2352711025000895Pushover analysisSeismic response Machine LearningReinforced concrete frame buildings |
| spellingShingle | Carlos Angarita Carlos Montes Orlando Arroyo Pushover-ML: A Machine Learning approach to predict a trilinear approximation of pushover curves for low-rise reinforced concrete frame buildings SoftwareX Pushover analysis Seismic response Machine Learning Reinforced concrete frame buildings |
| title | Pushover-ML: A Machine Learning approach to predict a trilinear approximation of pushover curves for low-rise reinforced concrete frame buildings |
| title_full | Pushover-ML: A Machine Learning approach to predict a trilinear approximation of pushover curves for low-rise reinforced concrete frame buildings |
| title_fullStr | Pushover-ML: A Machine Learning approach to predict a trilinear approximation of pushover curves for low-rise reinforced concrete frame buildings |
| title_full_unstemmed | Pushover-ML: A Machine Learning approach to predict a trilinear approximation of pushover curves for low-rise reinforced concrete frame buildings |
| title_short | Pushover-ML: A Machine Learning approach to predict a trilinear approximation of pushover curves for low-rise reinforced concrete frame buildings |
| title_sort | pushover ml a machine learning approach to predict a trilinear approximation of pushover curves for low rise reinforced concrete frame buildings |
| topic | Pushover analysis Seismic response Machine Learning Reinforced concrete frame buildings |
| url | http://www.sciencedirect.com/science/article/pii/S2352711025000895 |
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