Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm
The main objective of this paper is to assess the vulnerability of reinforced concrete (RC) educational buildings in Dhaka city to seismic activity by utilizing machine learning (ML) algorithms. There are three main stages in traditional seismic vulnerability assessment: rapid visual assessment (RVA...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/2315316 |
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| author | Tapan Kumar Mohammad Al Amin Siddique Raquib Ahsan |
| author_facet | Tapan Kumar Mohammad Al Amin Siddique Raquib Ahsan |
| author_sort | Tapan Kumar |
| collection | DOAJ |
| description | The main objective of this paper is to assess the vulnerability of reinforced concrete (RC) educational buildings in Dhaka city to seismic activity by utilizing machine learning (ML) algorithms. There are three main stages in traditional seismic vulnerability assessment: rapid visual assessment (RVA), preliminary engineering assessment (PEA), and detailed engineering assessment (DEA). The conventional three-step evaluation process for determining the seismic vulnerability of existing buildings is time-consuming and expensive, especially when dealing with a large building stock or a city. This study focuses on using an ML-based approach to evaluate seismic vulnerability, specifically in terms of the story shear ratio (SSR), which serves as the risk index. The main concept is the utilization of RVA data to obtain analytical results (SSR). The dataset utilized in this study comprises RVA data for 268 buildings and corresponding PEA data for the same 268 buildings. The RVA data include the construction year, condition, typical floor area, number of stories, total floor area, additions, alterations, redundancy, pounding, and irregularities. The PEA data comprise SSR, which was generated from linear dynamic analysis. These data were collected from the Urban Resilience Project of Rajdhani Unnayan Kartripakkha (RAJUK), which is the development authority of Dhaka. Random forest regression (RFR), support vector regression (SVR), and artificial neural networks (ANNs) are employed to determine the SSR of existing educational RC buildings. A comparative analysis for each model is also made. From the analysis results, it shows that RFR, ANN, and SVR achieved coefficient of determination (R2) of 20%, 25%, and 35%, respectively. Based on the findings from the three separate model analyses, it can be concluded that SVR produced the highest performance among the considered models. |
| format | Article |
| id | doaj-art-c148b6e49c324d03a600e5b3055dce9c |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-c148b6e49c324d03a600e5b3055dce9c2025-02-03T11:28:18ZengWileyAdvances in Civil Engineering1687-80942024-01-01202410.1155/2024/2315316Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning AlgorithmTapan Kumar0Mohammad Al Amin Siddique1Raquib Ahsan2Department of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringThe main objective of this paper is to assess the vulnerability of reinforced concrete (RC) educational buildings in Dhaka city to seismic activity by utilizing machine learning (ML) algorithms. There are three main stages in traditional seismic vulnerability assessment: rapid visual assessment (RVA), preliminary engineering assessment (PEA), and detailed engineering assessment (DEA). The conventional three-step evaluation process for determining the seismic vulnerability of existing buildings is time-consuming and expensive, especially when dealing with a large building stock or a city. This study focuses on using an ML-based approach to evaluate seismic vulnerability, specifically in terms of the story shear ratio (SSR), which serves as the risk index. The main concept is the utilization of RVA data to obtain analytical results (SSR). The dataset utilized in this study comprises RVA data for 268 buildings and corresponding PEA data for the same 268 buildings. The RVA data include the construction year, condition, typical floor area, number of stories, total floor area, additions, alterations, redundancy, pounding, and irregularities. The PEA data comprise SSR, which was generated from linear dynamic analysis. These data were collected from the Urban Resilience Project of Rajdhani Unnayan Kartripakkha (RAJUK), which is the development authority of Dhaka. Random forest regression (RFR), support vector regression (SVR), and artificial neural networks (ANNs) are employed to determine the SSR of existing educational RC buildings. A comparative analysis for each model is also made. From the analysis results, it shows that RFR, ANN, and SVR achieved coefficient of determination (R2) of 20%, 25%, and 35%, respectively. Based on the findings from the three separate model analyses, it can be concluded that SVR produced the highest performance among the considered models.http://dx.doi.org/10.1155/2024/2315316 |
| spellingShingle | Tapan Kumar Mohammad Al Amin Siddique Raquib Ahsan Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm Advances in Civil Engineering |
| title | Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm |
| title_full | Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm |
| title_fullStr | Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm |
| title_full_unstemmed | Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm |
| title_short | Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm |
| title_sort | seismic vulnerability assessment of reinforced concrete educational buildings using machine learning algorithm |
| url | http://dx.doi.org/10.1155/2024/2315316 |
| work_keys_str_mv | AT tapankumar seismicvulnerabilityassessmentofreinforcedconcreteeducationalbuildingsusingmachinelearningalgorithm AT mohammadalaminsiddique seismicvulnerabilityassessmentofreinforcedconcreteeducationalbuildingsusingmachinelearningalgorithm AT raquibahsan seismicvulnerabilityassessmentofreinforcedconcreteeducationalbuildingsusingmachinelearningalgorithm |