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A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings
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| Veröffentlicht in: | Applied Sciences Volume 11 (2021), issue 16, article 7540 |
|---|---|
| Personen und Körperschaften: | , , , , , |
| Titel: | A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings/ Ehsan Harirchian, Vandana Kumari, Kirti Jadhav, Shahla Rasulzade, Tom Lahmer, Rohan Raj Das |
| Format: | E-Book-Kapitel |
| Sprache: | Englisch |
| veröffentlicht: |
17.08.2021
|
| Gesamtaufnahme: |
: Applied Sciences, Volume 11 (2021), issue 16, article 7540
, volume:11 |
| Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
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| 245 | 1 | 0 | |a A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings |c Ehsan Harirchian, Vandana Kumari, Kirti Jadhav, Shahla Rasulzade, Tom Lahmer, Rohan Raj Das |
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| 520 | |a A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings. | ||
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| contents | A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings. |
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| spelling | Harirchian, Ehsan VerfasserIn (DE-588)122005822X (DE-627)1736256203 aut, A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings Ehsan Harirchian, Vandana Kumari, Kirti Jadhav, Shahla Rasulzade, Tom Lahmer, Rohan Raj Das, 17.08.2021, Illustrationen, Diagramme, 33, Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings., Kumari, Vandana VerfasserIn aut, Jadhav, Kirti VerfasserIn aut, Rasulzade, Shahla VerfasserIn aut, Lahmer, Tom 1978- VerfasserIn (DE-588)135943620 (DE-627)573391718 (DE-576)300736258 aut, Raj Das, Rohan VerfasserIn aut, Enthalten in Applied Sciences Basel : MDPI, 2011 Volume 11 (2021), issue 16, article 7540 Online-Ressource (DE-627)737287640 (DE-600)2704225-X (DE-576)379466716 2076-3417 nnns, volume:11 year:2021 number:16 elocationid:7540 extent:33, https://doi.org/10.3390/app11167540 application/pdf Resolving-System kostenfrei Volltext, https://doi.org/10.3390/app11167540 LFER, LFER 2021-10-05T10:02:40Z |
| spellingShingle | Harirchian, Ehsan, Kumari, Vandana, Jadhav, Kirti, Rasulzade, Shahla, Lahmer, Tom, Raj Das, Rohan, A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings, A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings. |
| title | A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings |
| title_auth | A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings |
| title_full | A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings Ehsan Harirchian, Vandana Kumari, Kirti Jadhav, Shahla Rasulzade, Tom Lahmer, Rohan Raj Das |
| title_fullStr | A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings Ehsan Harirchian, Vandana Kumari, Kirti Jadhav, Shahla Rasulzade, Tom Lahmer, Rohan Raj Das |
| title_full_unstemmed | A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings Ehsan Harirchian, Vandana Kumari, Kirti Jadhav, Shahla Rasulzade, Tom Lahmer, Rohan Raj Das |
| title_in_hierarchy | A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings / Ehsan Harirchian, Vandana Kumari, Kirti Jadhav, Shahla Rasulzade, Tom Lahmer, Rohan Raj Das, |
| title_short | A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings |
| title_sort | a synthesized study based on machine learning approaches for rapid classifying earthquake damage grades to rc buildings |
| url | https://doi.org/10.3390/app11167540 |