Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques
The dynamic analysis of structures is a computationally intensive procedure that must be considered, in order to make accurate seismic performance assessments in civil and structural engineering applications. To avoid these computationally demanding tasks, simplified methods are often used by engine...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/11/7/126 |
| _version_ | 1797589760963772416 |
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| author | Manolis Georgioudakis Vagelis Plevris |
| author_facet | Manolis Georgioudakis Vagelis Plevris |
| author_sort | Manolis Georgioudakis |
| collection | DOAJ |
| description | The dynamic analysis of structures is a computationally intensive procedure that must be considered, in order to make accurate seismic performance assessments in civil and structural engineering applications. To avoid these computationally demanding tasks, simplified methods are often used by engineers in practice, to estimate the behavior of complex structures under dynamic loading. This paper presents an assessment of several machine learning (ML) algorithms, with different characteristics, that aim to predict the dynamic analysis response of multi-story buildings. Large datasets of dynamic response analyses results were generated through standard sampling methods and conventional response spectrum modal analysis procedures. In an effort to obtain the best algorithm performance, an extensive hyper-parameter search was elaborated, followed by the corresponding feature importance. The ML model which exhibited the best performance was deployed in a web application, with the aim of providing predictions of the dynamic responses of multi-story buildings, according to their characteristics. |
| first_indexed | 2024-03-11T01:11:03Z |
| format | Article |
| id | doaj.art-bf23230c193c4cb2ab79c9ce5cdd8678 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-11T01:11:03Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-bf23230c193c4cb2ab79c9ce5cdd86782023-11-18T18:52:00ZengMDPI AGComputation2079-31972023-06-0111712610.3390/computation11070126Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning TechniquesManolis Georgioudakis0Vagelis Plevris1Institute of Structural Analysis & Antiseismic Research, School of Civil Engineering, National Technical University of Athens, Zografou Campus, GR 15780 Athens, GreeceDepartment of Civil and Environmental Engineering, Qatar University, Doha P.O. Box 2713, QatarThe dynamic analysis of structures is a computationally intensive procedure that must be considered, in order to make accurate seismic performance assessments in civil and structural engineering applications. To avoid these computationally demanding tasks, simplified methods are often used by engineers in practice, to estimate the behavior of complex structures under dynamic loading. This paper presents an assessment of several machine learning (ML) algorithms, with different characteristics, that aim to predict the dynamic analysis response of multi-story buildings. Large datasets of dynamic response analyses results were generated through standard sampling methods and conventional response spectrum modal analysis procedures. In an effort to obtain the best algorithm performance, an extensive hyper-parameter search was elaborated, followed by the corresponding feature importance. The ML model which exhibited the best performance was deployed in a web application, with the aim of providing predictions of the dynamic responses of multi-story buildings, according to their characteristics.https://www.mdpi.com/2079-3197/11/7/126response spectrum analysisensemble algorithmsmachine learningshear buildingSHAP explainability |
| spellingShingle | Manolis Georgioudakis Vagelis Plevris Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques Computation response spectrum analysis ensemble algorithms machine learning shear building SHAP explainability |
| title | Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques |
| title_full | Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques |
| title_fullStr | Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques |
| title_full_unstemmed | Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques |
| title_short | Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques |
| title_sort | response spectrum analysis of multi story shear buildings using machine learning techniques |
| topic | response spectrum analysis ensemble algorithms machine learning shear building SHAP explainability |
| url | https://www.mdpi.com/2079-3197/11/7/126 |
| work_keys_str_mv | AT manolisgeorgioudakis responsespectrumanalysisofmultistoryshearbuildingsusingmachinelearningtechniques AT vagelisplevris responsespectrumanalysisofmultistoryshearbuildingsusingmachinelearningtechniques |