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Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms

This study compares Bayesian Optimization-based machine learning systems that anticipate earthquake-damaged buildings and to evaluates building damage classification models. Using metrics, this study evaluates Random Forest, ElasticNet, and Decision Tree algorithms. This study showed damage level as...

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Main Authors: Al-Rawashdeh, Mohammad, Al Nawaiseh, Moh’d, Yousef, Isam, Bisharah, Majdi, Alkhadrawi, Sajeda, Al-Bdour, Hamza
Format: Article
Published: Springer Cham 2024
Online Access:http://psasir.upm.edu.my/id/eprint/105841/
https://link.springer.com/article/10.1007/s42107-023-00771-6
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spelling my.upm.eprints.1058412024-05-08T23:29:29Z http://psasir.upm.edu.my/id/eprint/105841/ Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms Al-Rawashdeh, Mohammad Al Nawaiseh, Moh’d Yousef, Isam Bisharah, Majdi Alkhadrawi, Sajeda Al-Bdour, Hamza This study compares Bayesian Optimization-based machine learning systems that anticipate earthquake-damaged buildings and to evaluates building damage classification models. Using metrics, this study evaluates Random Forest, ElasticNet, and Decision Tree algorithms. This study showed damage level asymmetry. Fifth grade is the most prevalent and first grade the least. The class imbalance makes estimating building damage grades difficult, emphasizing the necessity for careful modeling. Bayesian Optimization optimizes machine learning algorithm hyperparameters to solve this problem. The optimization technique maximizes the receiver operating characteristic curve (AUROC), which measures the models’ ability to discern between damage levels. Convergence shows that Bayesian Optimization improves model discrimination. The optimized models classified building damage grades with an AUROC of 0.9952. Comparing machine learning algorithms yields insights. The ElasticNet model predicts building damage grade with 92.56 test accuracy and 92.67 train accuracy. With 89.39 test accuracy and 99.82 train accuracy, the Random Forest model performs well. The Decision Tree model has 89.19 test and 99.94 train accuracy. Mean Squared Error (MSE) shows that the Random Forest model makes more accurate predictions than the others. In this study, machine learning techniques forecast building deterioration. Research should address class imbalance, because it affects model performance. Bayesian Optimization helps models acquire data patterns, improving classification accuracy. This study shows that machine learning and optimization can forecast building seismic damage grades. The proposed model can successfully use for earthquake risk assessment and mitigation. Springer Cham 2024 Article PeerReviewed Al-Rawashdeh, Mohammad and Al Nawaiseh, Moh’d and Yousef, Isam and Bisharah, Majdi and Alkhadrawi, Sajeda and Al-Bdour, Hamza (2024) Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms. Asian Journal of Civil Engineering, 25 (1). pp. 253-264. ISSN 1563-0854; ESSN: 2522-011X https://link.springer.com/article/10.1007/s42107-023-00771-6 10.1007/s42107-023-00771-6
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description This study compares Bayesian Optimization-based machine learning systems that anticipate earthquake-damaged buildings and to evaluates building damage classification models. Using metrics, this study evaluates Random Forest, ElasticNet, and Decision Tree algorithms. This study showed damage level asymmetry. Fifth grade is the most prevalent and first grade the least. The class imbalance makes estimating building damage grades difficult, emphasizing the necessity for careful modeling. Bayesian Optimization optimizes machine learning algorithm hyperparameters to solve this problem. The optimization technique maximizes the receiver operating characteristic curve (AUROC), which measures the models’ ability to discern between damage levels. Convergence shows that Bayesian Optimization improves model discrimination. The optimized models classified building damage grades with an AUROC of 0.9952. Comparing machine learning algorithms yields insights. The ElasticNet model predicts building damage grade with 92.56 test accuracy and 92.67 train accuracy. With 89.39 test accuracy and 99.82 train accuracy, the Random Forest model performs well. The Decision Tree model has 89.19 test and 99.94 train accuracy. Mean Squared Error (MSE) shows that the Random Forest model makes more accurate predictions than the others. In this study, machine learning techniques forecast building deterioration. Research should address class imbalance, because it affects model performance. Bayesian Optimization helps models acquire data patterns, improving classification accuracy. This study shows that machine learning and optimization can forecast building seismic damage grades. The proposed model can successfully use for earthquake risk assessment and mitigation.
format Article
author Al-Rawashdeh, Mohammad
Al Nawaiseh, Moh’d
Yousef, Isam
Bisharah, Majdi
Alkhadrawi, Sajeda
Al-Bdour, Hamza
spellingShingle Al-Rawashdeh, Mohammad
Al Nawaiseh, Moh’d
Yousef, Isam
Bisharah, Majdi
Alkhadrawi, Sajeda
Al-Bdour, Hamza
Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
author_facet Al-Rawashdeh, Mohammad
Al Nawaiseh, Moh’d
Yousef, Isam
Bisharah, Majdi
Alkhadrawi, Sajeda
Al-Bdour, Hamza
author_sort Al-Rawashdeh, Mohammad
title Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
title_short Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
title_full Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
title_fullStr Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
title_full_unstemmed Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
title_sort predicting building damage grade by earthquake: a bayesian optimization-based comparative study of machine learning algorithms
publisher Springer Cham
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/105841/
https://link.springer.com/article/10.1007/s42107-023-00771-6
_version_ 1800093787537539072
score 13.211869

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