An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced Pounding
Earthquake-induced structural pounding may cause major damages to structures, and therefore it should be prevented. This study is focused on using an artificial neural network (ANN) method to determine the sufficient seismic gap in order to avoid collisions between two adjacent buildings during seis...
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| Format: | Article |
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MDPI AG
2020-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/10/3591 |
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| author | Seyed Mohammad Khatami Hosein Naderpour Seyed Mohammad Nazem Razavi Rui Carneiro Barros Barbara Sołtysik Robert Jankowski |
| author_facet | Seyed Mohammad Khatami Hosein Naderpour Seyed Mohammad Nazem Razavi Rui Carneiro Barros Barbara Sołtysik Robert Jankowski |
| author_sort | Seyed Mohammad Khatami |
| collection | DOAJ |
| description | Earthquake-induced structural pounding may cause major damages to structures, and therefore it should be prevented. This study is focused on using an artificial neural network (ANN) method to determine the sufficient seismic gap in order to avoid collisions between two adjacent buildings during seismic excitations. Six lumped mass models of structures with a different number of stories (from one to six) have been considered in the study. The earthquake characteristics and the parameters of buildings have been defined as inputs in the ANN analysis. The required seismic gap preventing pounding has been firstly determined for specified structural arrangements and earthquake records. In order to validate the method for other structural parameters, the study has been further extended for buildings with different values of height, mass, and stiffness of each story. Finally, the parametric analysis has been conducted for various earthquakes scaled to different values of the peak ground acceleration (PGA). The results of the verification and validation analyses indicate that the determined seismic gaps are large enough to prevent structural collisions, and they are just appropriate for all different structural arrangements, seismic excitations, and structural parameters. The results of the parametric analysis show that the increase in the PGA of earthquake records leads to a substantial, nearly uniform, increase in the required seismic gap between structures. The above conclusions clearly indicate that the ANN method can be successfully used to determine the minimal distance between two adjacent buildings preventing their collisions during different seismic excitations. |
| first_indexed | 2024-03-10T19:40:08Z |
| format | Article |
| id | doaj.art-da87fe058b4f46938f0f90636e3f3b31 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:40:08Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-da87fe058b4f46938f0f90636e3f3b312023-11-20T01:23:40ZengMDPI AGApplied Sciences2076-34172020-05-011010359110.3390/app10103591An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced PoundingSeyed Mohammad Khatami0Hosein Naderpour1Seyed Mohammad Nazem Razavi2Rui Carneiro Barros3Barbara Sołtysik4Robert Jankowski5Center of Semnan Municipality, University of Applied Science and Technology, Semnan 98 23, IranFaculty of Civil Engineering, Semnan University, Semnan 3513119111, IranFaculty of Civil Engineering, Isfahan University, Isfahan 031, IranFaculty of Engineering, University of Porto (FEUP), 351-22 Porto, PortugalFaculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, PolandFaculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, PolandEarthquake-induced structural pounding may cause major damages to structures, and therefore it should be prevented. This study is focused on using an artificial neural network (ANN) method to determine the sufficient seismic gap in order to avoid collisions between two adjacent buildings during seismic excitations. Six lumped mass models of structures with a different number of stories (from one to six) have been considered in the study. The earthquake characteristics and the parameters of buildings have been defined as inputs in the ANN analysis. The required seismic gap preventing pounding has been firstly determined for specified structural arrangements and earthquake records. In order to validate the method for other structural parameters, the study has been further extended for buildings with different values of height, mass, and stiffness of each story. Finally, the parametric analysis has been conducted for various earthquakes scaled to different values of the peak ground acceleration (PGA). The results of the verification and validation analyses indicate that the determined seismic gaps are large enough to prevent structural collisions, and they are just appropriate for all different structural arrangements, seismic excitations, and structural parameters. The results of the parametric analysis show that the increase in the PGA of earthquake records leads to a substantial, nearly uniform, increase in the required seismic gap between structures. The above conclusions clearly indicate that the ANN method can be successfully used to determine the minimal distance between two adjacent buildings preventing their collisions during different seismic excitations.https://www.mdpi.com/2076-3417/10/10/3591seismic gapstructural poundingearthquakesartificial neural network |
| spellingShingle | Seyed Mohammad Khatami Hosein Naderpour Seyed Mohammad Nazem Razavi Rui Carneiro Barros Barbara Sołtysik Robert Jankowski An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced Pounding Applied Sciences seismic gap structural pounding earthquakes artificial neural network |
| title | An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced Pounding |
| title_full | An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced Pounding |
| title_fullStr | An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced Pounding |
| title_full_unstemmed | An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced Pounding |
| title_short | An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced Pounding |
| title_sort | ann based approach for prediction of sufficient seismic gap between adjacent buildings prone to earthquake induced pounding |
| topic | seismic gap structural pounding earthquakes artificial neural network |
| url | https://www.mdpi.com/2076-3417/10/10/3591 |
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