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A platform for research: civil engineering, architecture and urbanism
Probabilistic prediction of engineering demand parameters using Bayesian inference approach
https://orcid.org/0000-0003-0077-6434
Abstract Nonlinear time history analysis is the most accurate method for obtaining engineering demand parameters, but it is also the most time-consuming. Therefore, only a limited number of ground motions are typically used, which increases uncertainty and is highly dependent on the ground motion selection. It is critical to obtain an accurate probability density function using risk-based methods. As a result, a Bayesian inference approach that employs a significant number of linear analyses to form beliefs is proposed in this paper. First, the posterior distributions obtained from a Bayesian model are used to form informative prior distributions in a new model based on non-informative prior and linear analysis results. The Bayesian model is then updated with a new set of limited number of nonlinear responses. As a result, it is possible to simulate the artificial responses using Monte-Carlo simulations to develop the probability density functions. Furthermore, by considering the desired number of intensity measures and without the need to separately examine their efficiency and sufficiency, the proposed method reduces the uncertainty associated with selecting a specific intensity measure. The proposed method, when applied to three sample buildings with three different hazard levels, shows a significant improvement in statistical prediction and produces reliable probability density functions. Furthermore, the sensitivity to ground motion selection is significantly reduced. Case studies demonstrate the proposed method's insensitivity not only at different hazard levels, but also at different building heights.
Graphical abstract Display Omitted
Probabilistic prediction of engineering demand parameters using Bayesian inference approach
https://orcid.org/0000-0003-0077-6434
Abstract Nonlinear time history analysis is the most accurate method for obtaining engineering demand parameters, but it is also the most time-consuming. Therefore, only a limited number of ground motions are typically used, which increases uncertainty and is highly dependent on the ground motion selection. It is critical to obtain an accurate probability density function using risk-based methods. As a result, a Bayesian inference approach that employs a significant number of linear analyses to form beliefs is proposed in this paper. First, the posterior distributions obtained from a Bayesian model are used to form informative prior distributions in a new model based on non-informative prior and linear analysis results. The Bayesian model is then updated with a new set of limited number of nonlinear responses. As a result, it is possible to simulate the artificial responses using Monte-Carlo simulations to develop the probability density functions. Furthermore, by considering the desired number of intensity measures and without the need to separately examine their efficiency and sufficiency, the proposed method reduces the uncertainty associated with selecting a specific intensity measure. The proposed method, when applied to three sample buildings with three different hazard levels, shows a significant improvement in statistical prediction and produces reliable probability density functions. Furthermore, the sensitivity to ground motion selection is significantly reduced. Case studies demonstrate the proposed method's insensitivity not only at different hazard levels, but also at different building heights.
Graphical abstract Display Omitted
Probabilistic prediction of engineering demand parameters using Bayesian inference approach
https://orcid.org/0000-0003-0077-6434
Abstract Nonlinear time history analysis is the most accurate method for obtaining engineering demand parameters, but it is also the most time-consuming. Therefore, only a limited number of ground motions are typically used, which increases uncertainty and is highly dependent on the ground motion selection. It is critical to obtain an accurate probability density function using risk-based methods. As a result, a Bayesian inference approach that employs a significant number of linear analyses to form beliefs is proposed in this paper. First, the posterior distributions obtained from a Bayesian model are used to form informative prior distributions in a new model based on non-informative prior and linear analysis results. The Bayesian model is then updated with a new set of limited number of nonlinear responses. As a result, it is possible to simulate the artificial responses using Monte-Carlo simulations to develop the probability density functions. Furthermore, by considering the desired number of intensity measures and without the need to separately examine their efficiency and sufficiency, the proposed method reduces the uncertainty associated with selecting a specific intensity measure. The proposed method, when applied to three sample buildings with three different hazard levels, shows a significant improvement in statistical prediction and produces reliable probability density functions. Furthermore, the sensitivity to ground motion selection is significantly reduced. Case studies demonstrate the proposed method's insensitivity not only at different hazard levels, but also at different building heights.
Graphical abstract Display Omitted
Probabilistic prediction of engineering demand parameters using Bayesian inference approach
Taheri, Shima (author) / Mohammadi, Reza Karami (author)
2022-04-22
Article (Journal)
Electronic Resource
English
Bayesian inference , Nonlinear time history analysis , Linear response spectrum analysis , Intensity measure , Engineering demand parameters , Demand prediction , Ground motion selection , Bayesian model averaging , BCI , Bayesian credible interval , BF , Bayes factor , BMA , BMC , Bayesian model comparison , CMS , Conditional mean spectrum , EDP , Engineering demand parameter , GLM , Generalized linear model , GM , Ground motion , IM , LRSA , MSD , Maximum story drift , MCMC , Markov chain Monte Carlo , NTHA , PBIM , Proposed Bayesian inference method , PCA , Principal component analysis , PDF , Probability density function , PFA , Peak floor acceleration , UHS , Uniform hazard spectrum
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