Seismic fragility analysis of bridges by relevance vector machine based demand prediction model

A relevance vector machine (RVM) based demand prediction model is explored for efficient seismic fragility analysis (SFA) of a bridge structure. The proposed RVM model integrates both record-to-record variations of ground motions and uncertainties of parameters characterizing the bridge model. For e...

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Bibliographic Details
Published in:Earthquake Engineering and Engineering Vibration 2022, Vol.21 (1), p.253-268
Main Authors: Ghosh, Swarup, Chakraborty, Subrata
Format: Article
Language:English
Subjects:
Bridges
Civil Engineering
Computation
Confidence intervals
Control
Critical components
Demand
Direct simulation Monte Carlo method
Dynamical Systems
Earth and Environmental Science
Earth Sciences
Estimates
Fragility
Geotechnical Engineering & Applied Earth Sciences
Ground motion
Hypercubes
Latin hypercube sampling
Machine learning
Mathematical models
Modelling
Monte Carlo simulation
Parameter uncertainty
Parameters
Prediction models
Sampling techniques
Seismic analysis
Uncertainty
Vibration
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Summary:A relevance vector machine (RVM) based demand prediction model is explored for efficient seismic fragility analysis (SFA) of a bridge structure. The proposed RVM model integrates both record-to-record variations of ground motions and uncertainties of parameters characterizing the bridge model. For efficient fragility computation, ground motion intensity is included as an added dimension to the demand prediction model. To incorporate different sources of uncertainty, random realizations of different structural parameters are generated using Latin hypercube sampling technique. Mean fragility, along with its dispersions, is estimated based on the log-normal fragility model for different critical components of a bridge. The effectiveness of the proposed RVM model-based SFA of a bridge structure is elucidated numerically by comparing it with fragility results obtained by the commonly used SFA approaches, while considering the most accurate direct Monte Carlo simulation-based fragility estimates as the benchmark. The proposed RVM model provides a more accurate estimate of fragility than conventional approaches, with significantly less computational effort. In addition, the proposed model provides a measure of uncertainty in fragility estimates by constructing confidence intervals for the fragility curves.
ISSN:1671-3664
1993-503X
DOI:10.1007/s11803-022-2082-7