Appraising the Risk Assessment of Non-Structural Components via Simplified and Machine-Learning-Based Approaches

Uncertainties in building element modelling and ground motions are indispensable for rigorous seismic risk quantification. demand-intensity models simply relate building performance to intensity and subsequently risk. This article describes developments toward non-structural element (NSEs) risk quan...

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Bibliographic Details
Published inJournal of earthquake engineering : JEE Vol. 28; no. 9; pp. 2440 - 2463
Main Authors Shahnazaryan, Davit, O'Reilly, Gerard J.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 03.07.2024
Taylor & Francis Ltd
Subjects
Building components
Buildings
Concrete
Datasets
Demand
demand-intensity
Environmental risk
Ground motion
Learning algorithms
Machine learning
Modelling
Nonstructural members
Numerical models
RC buildings
Reinforced concrete
Risk assessment
Seismic hazard
Seismic risk; non-structural elements
Structural analysis
Structural members
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Summary:Uncertainties in building element modelling and ground motions are indispensable for rigorous seismic risk quantification. demand-intensity models simply relate building performance to intensity and subsequently risk. This article describes developments toward non-structural element (NSEs) risk quantification. The demand-intensity models are applied to infilled reinforced concrete (RC) case-study buildings. Implicit and explicit NSE numerical modelling is used to validate the models versus the direct integration of risk exceedance. For faster estimation techniques, machine learning models are trained to estimate the demand-intensity model fitting parameters on a modest dataset of RC infilled buildings. Among these, the extreme gradient boosting (XGBoost) demonstrated superior performance, indicating further possible directions for improvement where larger datasets are available. These models facilitate the simple retrieving of demand-intensity models without extensive structural analysis and can be utilized for both structural and non-structural elements when assessing risk in single or multiple buildings as part of portfolio analysis.
ISSN:1363-2469
1559-808X
DOI:10.1080/13632469.2024.2314169