Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Explainable Machine Learning for Seismic Vulnerability Assessment of Low-Rise Reinforced Concrete Buildings
Seismic vulnerability assessment empowers decision-makers to develop effective earthquake mitigation strategies. In the past few decades, numerous empirical and analytical seismic vulnerability methods have been proposed; among which is the Hassan-Sozen Priority Index (PI). The PI is a very simple and popular metric for the seismic vulnerability of low-rise reinforced concrete buildings based on a linear combination of only the geometric dimensions of a building such as the floor area and the size of the lateral force resisting elements. This simplicity makes the PI a useful preliminary screening tool, but its accuracy and reliability are limited and questionable since the index oversimplifies the relationship between the dimensions of the building and its potential damage rating. Moreover, the performance of the PI has only been validated using a small set of post-earthquake reconnaissance data. State-of-the-art machine learning techniques make it possible to efficiently fit large and complex datasets which opens the door for the development of more advanced seismic vulnerability assessment methodologies. In this study, a large volume of post-earthquake reconnaissance data is gathered and an ensemble supervised learning approach (Random Forest) is implemented to develop a robust model that can relate the PI input parameters to building damage rating. Additionally, a Shapley Additive explanations (SHAP) method is exploited to validate and explain the models and conduct model transparency analysis.
Explainable Machine Learning for Seismic Vulnerability Assessment of Low-Rise Reinforced Concrete Buildings
Seismic vulnerability assessment empowers decision-makers to develop effective earthquake mitigation strategies. In the past few decades, numerous empirical and analytical seismic vulnerability methods have been proposed; among which is the Hassan-Sozen Priority Index (PI). The PI is a very simple and popular metric for the seismic vulnerability of low-rise reinforced concrete buildings based on a linear combination of only the geometric dimensions of a building such as the floor area and the size of the lateral force resisting elements. This simplicity makes the PI a useful preliminary screening tool, but its accuracy and reliability are limited and questionable since the index oversimplifies the relationship between the dimensions of the building and its potential damage rating. Moreover, the performance of the PI has only been validated using a small set of post-earthquake reconnaissance data. State-of-the-art machine learning techniques make it possible to efficiently fit large and complex datasets which opens the door for the development of more advanced seismic vulnerability assessment methodologies. In this study, a large volume of post-earthquake reconnaissance data is gathered and an ensemble supervised learning approach (Random Forest) is implemented to develop a robust model that can relate the PI input parameters to building damage rating. Additionally, a Shapley Additive explanations (SHAP) method is exploited to validate and explain the models and conduct model transparency analysis.
Explainable Machine Learning for Seismic Vulnerability Assessment of Low-Rise Reinforced Concrete Buildings
Lecture Notes in Civil Engineering
Walbridge, Scott (Herausgeber:in) / Nik-Bakht, Mazdak (Herausgeber:in) / Ng, Kelvin Tsun Wai (Herausgeber:in) / Shome, Manas (Herausgeber:in) / Alam, M. Shahria (Herausgeber:in) / el Damatty, Ashraf (Herausgeber:in) / Lovegrove, Gordon (Herausgeber:in) / Midwinter, M. (Autor:in) / Yeum, C. M. (Autor:in) / Kim, E. (Autor:in)
Canadian Society of Civil Engineering Annual Conference ; 2021
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 ; Kapitel: 31 ; 371-379
14.04.2022
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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