Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings

The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of ear...
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Autor*in:	Vandana Kumari [verfasserIn] Ehsan Harirchian [verfasserIn] Tom Lahmer [verfasserIn] Shahla Rasulzade [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	rapid assessment machine learning seismic vulnerability Django damage classification

Übergeordnetes Werk:	In: Buildings - MDPI AG, 2012, 12(2022), 5, p 578
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:5, p 578

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/buildings12050578

Katalog-ID:	DOAJ040789594

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spelling	10.3390/buildings12050578 doi (DE-627)DOAJ040789594 (DE-599)DOAJde59ae1345614366af4225d63e5dec25 DE-627 ger DE-627 rakwb eng TH1-9745 Vandana Kumari verfasserin aut Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency. rapid assessment machine learning seismic vulnerability Django damage classification Building construction Ehsan Harirchian verfasserin aut Tom Lahmer verfasserin aut Shahla Rasulzade verfasserin aut In Buildings MDPI AG, 2012 12(2022), 5, p 578 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:12 year:2022 number:5, p 578 https://doi.org/10.3390/buildings12050578 kostenfrei https://doaj.org/article/de59ae1345614366af4225d63e5dec25 kostenfrei https://www.mdpi.com/2075-5309/12/5/578 kostenfrei https://doaj.org/toc/2075-5309 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2022 5, p 578
allfields_unstemmed	10.3390/buildings12050578 doi (DE-627)DOAJ040789594 (DE-599)DOAJde59ae1345614366af4225d63e5dec25 DE-627 ger DE-627 rakwb eng TH1-9745 Vandana Kumari verfasserin aut Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency. rapid assessment machine learning seismic vulnerability Django damage classification Building construction Ehsan Harirchian verfasserin aut Tom Lahmer verfasserin aut Shahla Rasulzade verfasserin aut In Buildings MDPI AG, 2012 12(2022), 5, p 578 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:12 year:2022 number:5, p 578 https://doi.org/10.3390/buildings12050578 kostenfrei https://doaj.org/article/de59ae1345614366af4225d63e5dec25 kostenfrei https://www.mdpi.com/2075-5309/12/5/578 kostenfrei https://doaj.org/toc/2075-5309 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2022 5, p 578
allfieldsGer	10.3390/buildings12050578 doi (DE-627)DOAJ040789594 (DE-599)DOAJde59ae1345614366af4225d63e5dec25 DE-627 ger DE-627 rakwb eng TH1-9745 Vandana Kumari verfasserin aut Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency. rapid assessment machine learning seismic vulnerability Django damage classification Building construction Ehsan Harirchian verfasserin aut Tom Lahmer verfasserin aut Shahla Rasulzade verfasserin aut In Buildings MDPI AG, 2012 12(2022), 5, p 578 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:12 year:2022 number:5, p 578 https://doi.org/10.3390/buildings12050578 kostenfrei https://doaj.org/article/de59ae1345614366af4225d63e5dec25 kostenfrei https://www.mdpi.com/2075-5309/12/5/578 kostenfrei https://doaj.org/toc/2075-5309 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2022 5, p 578
allfieldsSound	10.3390/buildings12050578 doi (DE-627)DOAJ040789594 (DE-599)DOAJde59ae1345614366af4225d63e5dec25 DE-627 ger DE-627 rakwb eng TH1-9745 Vandana Kumari verfasserin aut Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency. rapid assessment machine learning seismic vulnerability Django damage classification Building construction Ehsan Harirchian verfasserin aut Tom Lahmer verfasserin aut Shahla Rasulzade verfasserin aut In Buildings MDPI AG, 2012 12(2022), 5, p 578 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:12 year:2022 number:5, p 578 https://doi.org/10.3390/buildings12050578 kostenfrei https://doaj.org/article/de59ae1345614366af4225d63e5dec25 kostenfrei https://www.mdpi.com/2075-5309/12/5/578 kostenfrei https://doaj.org/toc/2075-5309 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2022 5, p 578
language	English
source	In Buildings 12(2022), 5, p 578 volume:12 year:2022 number:5, p 578
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	rapid assessment machine learning seismic vulnerability Django damage classification Building construction
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authorswithroles_txt_mv	Vandana Kumari @@aut@@ Ehsan Harirchian @@aut@@ Tom Lahmer @@aut@@ Shahla Rasulzade @@aut@@
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callnumber-first	T - Technology
author	Vandana Kumari
spellingShingle	Vandana Kumari misc TH1-9745 misc rapid assessment misc machine learning misc seismic vulnerability misc Django misc damage classification misc Building construction Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
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topic_title	TH1-9745 Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings rapid assessment machine learning seismic vulnerability Django damage classification
topic	misc TH1-9745 misc rapid assessment misc machine learning misc seismic vulnerability misc Django misc damage classification misc Building construction
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title	Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
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title_full	Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
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title_sort	evaluation of machine learning and web-based process for damage score estimation of existing buildings
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title_auth	Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
abstract	The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency.
abstractGer	The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency.
abstract_unstemmed	The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency.
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title_short	Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
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Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings

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