A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm
In the current manuscript, a novel software application for rapid damage assessment of RC buildings subjected to earthquake excitation is presented based on artificial neural networks. The software integrates the use of a novel deep learning methodology for rapid damage assessment into modern softwa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/8/5100 |
| _version_ | 1797606474598318080 |
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| author | Konstantinos Morfidis Sotiria Stefanidou Olga Markogiannaki |
| author_facet | Konstantinos Morfidis Sotiria Stefanidou Olga Markogiannaki |
| author_sort | Konstantinos Morfidis |
| collection | DOAJ |
| description | In the current manuscript, a novel software application for rapid damage assessment of RC buildings subjected to earthquake excitation is presented based on artificial neural networks. The software integrates the use of a novel deep learning methodology for rapid damage assessment into modern software development platforms, while the developed graphical user interface promotes the ease of use even from non-experts. The aim is to foster actions both in the pre- and post-earthquake phase. The structure of the source code permits the usage of the application either autonomously as a software tool for rapid visual inspections of buildings prior to or after a strong seismic event or as a component of building information modelling systems in the framework of digitizing building data and properties. The methodology implemented for the estimation of the RC buildings’ damage states is based on the theory and algorithms of pattern recognition problems. The effectiveness of the developed software is successfully tested using an extended, numerically generated database of RC buildings subjected to recorded seismic events. |
| first_indexed | 2024-03-11T05:15:42Z |
| format | Article |
| id | doaj.art-2817852248a84f4685ada7a688e42b56 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:15:42Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2817852248a84f4685ada7a688e42b562023-11-17T18:13:39ZengMDPI AGApplied Sciences2076-34172023-04-01138510010.3390/app13085100A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence AlgorithmKonstantinos Morfidis0Sotiria Stefanidou1Olga Markogiannaki2Earthquake Planning and Protection Organization (EPPO-ITSAK), Terma Dasylliou, 55535 Thessaloniki, GreeceDepartment of Civil Engineering, Aristotle University of Thessaloniki, Aristotle University Campus, 54124 Thessaloniki, GreeceDepartment of Civil Engineering, Aristotle University of Thessaloniki, Aristotle University Campus, 54124 Thessaloniki, GreeceIn the current manuscript, a novel software application for rapid damage assessment of RC buildings subjected to earthquake excitation is presented based on artificial neural networks. The software integrates the use of a novel deep learning methodology for rapid damage assessment into modern software development platforms, while the developed graphical user interface promotes the ease of use even from non-experts. The aim is to foster actions both in the pre- and post-earthquake phase. The structure of the source code permits the usage of the application either autonomously as a software tool for rapid visual inspections of buildings prior to or after a strong seismic event or as a component of building information modelling systems in the framework of digitizing building data and properties. The methodology implemented for the estimation of the RC buildings’ damage states is based on the theory and algorithms of pattern recognition problems. The effectiveness of the developed software is successfully tested using an extended, numerically generated database of RC buildings subjected to recorded seismic events.https://www.mdpi.com/2076-3417/13/8/5100seismic damage assessmentartificial neural networkspattern recognitionsoftware developmentRC buildings |
| spellingShingle | Konstantinos Morfidis Sotiria Stefanidou Olga Markogiannaki A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm Applied Sciences seismic damage assessment artificial neural networks pattern recognition software development RC buildings |
| title | A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm |
| title_full | A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm |
| title_fullStr | A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm |
| title_full_unstemmed | A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm |
| title_short | A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm |
| title_sort | rapid seismic damage assessment rasda tool for rc buildings based on an artificial intelligence algorithm |
| topic | seismic damage assessment artificial neural networks pattern recognition software development RC buildings |
| url | https://www.mdpi.com/2076-3417/13/8/5100 |
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