Fragility curves are one of the substantial means required for seismic risk assessment of buildings in the framework of performance-based earthquake engineering (PBEE). Obtaining fragility curves, however, needs an extensive analytical analysis which makes it time-consuming and sometimes inaccurate due to errors. In this study, hence, machine learning (ML)-based models are proposed for predicting fragility parameters of structures namely dispersion, beta, and mu (log (PGA)), based on a reduced set of information avoiding cumbersome computation. Firstly, a brief review on the analytical models proposed for deriving fragility curves is provided as well as ML-based models developed so far. Then, to achieve the research objective, a comprehensive database including 214 datasets from peer-reviewed international publications is collected. It is then divided into training (85%) and testing (15%) sub-datasets for the purpose of training the models and assessing the results, respectively. The most effective parameters on the target outputs are defined as input variables including construction material, building plan area, building height, damage state, buildings' period and soil classification. beta and mu were estimated utilizing various ML-based techniques namely nonlinear regression, decision tree, random forest, K-Nearest Neighbours (KNN) and Artificial Neural Network (ANN). The actual values and the values pre-dicted by the proposed models are compared. Moreover, the models' accuracy is assessed through performance metrics and Taylor diagram. The results demonstrated the high ability of the models for learning the relationship between inputs and outputs. According to the accuracy assessment, Decision tree was the most accurate model for predicting both beta and mu. A sensitivity study was also conducted by changing input variables, and estimation equations are provided accordingly.

A machine learning-based analysis for predicting fragility curve parameters of buildings

Dabiri, H
Primo
;
Dall'Asta, A;Tondi, E;Micozzi, F
Ultimo
2022-01-01

Abstract

Fragility curves are one of the substantial means required for seismic risk assessment of buildings in the framework of performance-based earthquake engineering (PBEE). Obtaining fragility curves, however, needs an extensive analytical analysis which makes it time-consuming and sometimes inaccurate due to errors. In this study, hence, machine learning (ML)-based models are proposed for predicting fragility parameters of structures namely dispersion, beta, and mu (log (PGA)), based on a reduced set of information avoiding cumbersome computation. Firstly, a brief review on the analytical models proposed for deriving fragility curves is provided as well as ML-based models developed so far. Then, to achieve the research objective, a comprehensive database including 214 datasets from peer-reviewed international publications is collected. It is then divided into training (85%) and testing (15%) sub-datasets for the purpose of training the models and assessing the results, respectively. The most effective parameters on the target outputs are defined as input variables including construction material, building plan area, building height, damage state, buildings' period and soil classification. beta and mu were estimated utilizing various ML-based techniques namely nonlinear regression, decision tree, random forest, K-Nearest Neighbours (KNN) and Artificial Neural Network (ANN). The actual values and the values pre-dicted by the proposed models are compared. Moreover, the models' accuracy is assessed through performance metrics and Taylor diagram. The results demonstrated the high ability of the models for learning the relationship between inputs and outputs. According to the accuracy assessment, Decision tree was the most accurate model for predicting both beta and mu. A sensitivity study was also conducted by changing input variables, and estimation equations are provided accordingly.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11581/467556
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