A novel and computationally efficient method for developing a nonparametric probabilistic seismic demand model(PSDM)is pro-posed to conduct the fragility analysis of subway stations accurately and efficiently.The prob...A novel and computationally efficient method for developing a nonparametric probabilistic seismic demand model(PSDM)is pro-posed to conduct the fragility analysis of subway stations accurately and efficiently.The probability density evolution method(PDEM)is used to calculate the evolutionary probability density function of demand measure(DM)without resort to any assumptions of the dis-tribution pattern of DM.To reduce the computational cost of a large amount of nonlinear time history analyses(NLTHAs)in the PDEM,the one-dimensional convolutional neural network(1D-CNN)is used as a surrogate model to predict the time history of struc-tural seismic responses in a data-driven fashion.The proposed nonparametric PSDM is adopted to conduct the fragility analysis of a two-story and three-span subway station,and the results are compared with those from two existing parametric PSDMs,i.e.,two-parameter lognormal distribution model and probabilistic neural network(PNN)-based PSDM.The results show that the PDEM-based PSDM has the best performance in describing the probability distribution of seismic responses of underground structures.Differ-ent from the fragility curves,the time-dependent fragility surface of the subway station shows how the exceedance probability of damage state changes over time.It can be used to estimate the escape time and thus the number of casualties in an earthquake,which are impor-tant indexes when conducting the resilience-based seismic evaluation.展开更多
Research of reliability of engineering structures has experienced a developing history for more than 90 years.However,the problem of how to resolve the global reliability of structural systems still remains open,espec...Research of reliability of engineering structures has experienced a developing history for more than 90 years.However,the problem of how to resolve the global reliability of structural systems still remains open,especially the problem of the combinatorial explosion and the challenge of correlation between failure modes.Benefiting from the research of probability density evolution theory in recent years,the physics-based system reliability researches open a new way for bypassing this dilemma.The present paper introduces the theoretical foundation of probability density evolution method in view of a broad background,whereby a probability density evolution equation for probability dissipative system is deduced.In conjunction of physical equations and structural failure criteria,a general engineering reliability analysis frame is then presented.For illustrative purposes,several cases are studied which prove the value of the proposed engineering reliability analysis method.展开更多
基金supported by National Key R&D Program of China(Grant No.2022YFE0104400)State Key Laboratory of Disaster Reduction in Civil Engineering(Grant No.SLDRCE19-B-38)the Fundamental Research Funds for the Central Universities,China(Grant No.22120210572).
文摘A novel and computationally efficient method for developing a nonparametric probabilistic seismic demand model(PSDM)is pro-posed to conduct the fragility analysis of subway stations accurately and efficiently.The probability density evolution method(PDEM)is used to calculate the evolutionary probability density function of demand measure(DM)without resort to any assumptions of the dis-tribution pattern of DM.To reduce the computational cost of a large amount of nonlinear time history analyses(NLTHAs)in the PDEM,the one-dimensional convolutional neural network(1D-CNN)is used as a surrogate model to predict the time history of struc-tural seismic responses in a data-driven fashion.The proposed nonparametric PSDM is adopted to conduct the fragility analysis of a two-story and three-span subway station,and the results are compared with those from two existing parametric PSDMs,i.e.,two-parameter lognormal distribution model and probabilistic neural network(PNN)-based PSDM.The results show that the PDEM-based PSDM has the best performance in describing the probability distribution of seismic responses of underground structures.Differ-ent from the fragility curves,the time-dependent fragility surface of the subway station shows how the exceedance probability of damage state changes over time.It can be used to estimate the escape time and thus the number of casualties in an earthquake,which are impor-tant indexes when conducting the resilience-based seismic evaluation.
文摘Research of reliability of engineering structures has experienced a developing history for more than 90 years.However,the problem of how to resolve the global reliability of structural systems still remains open,especially the problem of the combinatorial explosion and the challenge of correlation between failure modes.Benefiting from the research of probability density evolution theory in recent years,the physics-based system reliability researches open a new way for bypassing this dilemma.The present paper introduces the theoretical foundation of probability density evolution method in view of a broad background,whereby a probability density evolution equation for probability dissipative system is deduced.In conjunction of physical equations and structural failure criteria,a general engineering reliability analysis frame is then presented.For illustrative purposes,several cases are studied which prove the value of the proposed engineering reliability analysis method.
