在装配整体式剪力墙结构中,由于套筒灌浆连接的质量具有一定的随机性,势必影响结构的竖向连接性能和结构抗震性能。根据不同缺陷程度的套筒灌浆拉拔试验,建立了一套等效套筒灌浆缺陷连接承载力模型,并基于某实际工程结构,建立了装配整...在装配整体式剪力墙结构中,由于套筒灌浆连接的质量具有一定的随机性,势必影响结构的竖向连接性能和结构抗震性能。根据不同缺陷程度的套筒灌浆拉拔试验,建立了一套等效套筒灌浆缺陷连接承载力模型,并基于某实际工程结构,建立了装配整体式剪力墙结构有限元模型。通过考虑灌浆缺陷的随机性,赋予连接接头相应缺陷程度的力学连接性能,来反映套筒灌浆中可能存在的缺陷。通过非线性有限元分析并结合概率密度演化方法(probability density evolution method,PDEM)进行了结构随机非线性反应分析和可靠度评估。结果表明:在动力作用下,结构非线性与随机性具有明显的耦合效应;缺陷的随机性会随着时间的推移,逐渐放大对结构响应的影响;在不同的安全域内,结构的整体可靠度将存在较大的差异。展开更多
工程设计中往往需要同时处理固有不确定性与认知不确定性。对于固有不确定性分析与量化,国内外已有诸多研究,例如 Monte Carlo 方法、正交多项式展开理论和概率密度演化理论等。而对认知不确定性、特别是固有不确定性与认知不确定性耦...工程设计中往往需要同时处理固有不确定性与认知不确定性。对于固有不确定性分析与量化,国内外已有诸多研究,例如 Monte Carlo 方法、正交多项式展开理论和概率密度演化理论等。而对认知不确定性、特别是固有不确定性与认知不确定性耦合情况下的研究,则还相对缺乏。该文中,针对数据稀缺与数据更新导致的认知不确定性,首先分别引入 Bootstrap 方法和 Bayes 更新方法进行不确定性表征。在此基础上,结合基于概率密度演化-测度变换的两类不确定性量化统一理论新框架,提出了存在认知不确定性情况下的不确定性传播与可靠性分析高效方法及其具体数值算法。由此,给出了基于数据进行工程系统不确定性量化、传播与可靠性分析的基本途径。通过具有工程实际数据的 3 个工程实例分析,包括无限边坡稳定性分析、挡土墙稳定性分析和屋面桁架结构可靠性分析,验证了该文方法的精度和效率。展开更多
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
文摘在装配整体式剪力墙结构中,由于套筒灌浆连接的质量具有一定的随机性,势必影响结构的竖向连接性能和结构抗震性能。根据不同缺陷程度的套筒灌浆拉拔试验,建立了一套等效套筒灌浆缺陷连接承载力模型,并基于某实际工程结构,建立了装配整体式剪力墙结构有限元模型。通过考虑灌浆缺陷的随机性,赋予连接接头相应缺陷程度的力学连接性能,来反映套筒灌浆中可能存在的缺陷。通过非线性有限元分析并结合概率密度演化方法(probability density evolution method,PDEM)进行了结构随机非线性反应分析和可靠度评估。结果表明:在动力作用下,结构非线性与随机性具有明显的耦合效应;缺陷的随机性会随着时间的推移,逐渐放大对结构响应的影响;在不同的安全域内,结构的整体可靠度将存在较大的差异。
文摘工程设计中往往需要同时处理固有不确定性与认知不确定性。对于固有不确定性分析与量化,国内外已有诸多研究,例如 Monte Carlo 方法、正交多项式展开理论和概率密度演化理论等。而对认知不确定性、特别是固有不确定性与认知不确定性耦合情况下的研究,则还相对缺乏。该文中,针对数据稀缺与数据更新导致的认知不确定性,首先分别引入 Bootstrap 方法和 Bayes 更新方法进行不确定性表征。在此基础上,结合基于概率密度演化-测度变换的两类不确定性量化统一理论新框架,提出了存在认知不确定性情况下的不确定性传播与可靠性分析高效方法及其具体数值算法。由此,给出了基于数据进行工程系统不确定性量化、传播与可靠性分析的基本途径。通过具有工程实际数据的 3 个工程实例分析,包括无限边坡稳定性分析、挡土墙稳定性分析和屋面桁架结构可靠性分析,验证了该文方法的精度和效率。
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.
