Influence of frequency‐dependent soil–structure interaction on the fragility of R/C bridges          下载免费PDF全文

Nikolaos Lesgidis  Anastasios Sextos  Oh‐Sung Kwon 《地震工程与结构动力学》2017,46(1):139-158

Bridge performance under earthquake loading can be significantly influenced by the interaction between the structure and the supporting soil. Even though the frequency dependence of the interaction mentioned in this study has long been documented, the simplifying assumption that the dynamic stiffness is dominated by the mean or predominant excitation frequency is still commonly made, primarily as a result of the associated numerical difficulties when the analysis has to be performed in the time domain. This study makes use of the advanced lumped parameter models recently developed 1 in order to quantify the impact of the assumption on the predicted fragility of bridges mentioned in this study. This is achieved by comparing the predicted vulnerability for the case of a reference, well studied, actual bridge using both conventional, frequency‐independent, Kelvin–Voigt models and the aforementioned lumped parameter formulation. Analysis results demonstrate that the more refined consideration of frequency dependence of soil–structure interaction at the piers and the abutments of a bridge not only leads to different probabilities of failure for given intensity measures but also leads to different hierarchy and distribution of damage within the structure for the same set of earthquake ground motions even if the overall probability of exceeding a given damage state is the same. The paper concludes with the comparative assessment of the effect for different soil conditions, foundation configurations, and ground motion characteristics mentioned in this study along with the relevant analysis and design recommendations. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

Pre- and post-earthquake regional loss assessment using deep learning     

Taeyong Kim  Junho Song  Oh-Sung Kwon 《地震工程与结构动力学》2020,49(7):657-678

As urban systems become more highly sophisticated and interdependent, their vulnerability to earthquake events exhibits a significant level of uncertainties. Thus, community-level seismic risk assessments are indispensable to facilitate decision making for effective hazard mitigation and disaster responses. To this end, new frameworks for pre- and post-earthquake regional loss assessments are proposed using deep learning methods. First, to improve the accuracy of the response prediction of individual structures during the pre-earthquake loss assessment, a widely used nonlinear static procedure is replaced by the recently developed probabilistic deep neural network model. The variabilities of the nonlinear responses of a structural system given the seismic intensity can be quantified during the loss assessment process. Second, to facilitate near-real-time post-earthquake loss assessments, an adaptive algorithm, which identifies the optimal number and locations of sensors in a given urban area, is proposed. Using a deep neural network that estimates area-wide structural damage given the spatial distribution of the seismic intensity levels as a surrogate model, the algorithm adaptively places additional sensors at property lots at which errors from surrogate estimations of the structural damage are the greatest. Note that the surrogate model is constructed before earthquake events using simulated datasets. To test and demonstrate the proposed frameworks, the paper introduces thorough numerical investigations of two hypothetical urban communities. The proposed frameworks using the deep learning methods are expected to make critical advances in pre- and post-earthquake regional loss assessments.  相似文献