Many countries throughout the world have experienced large earthquakes,which cause building damage or collapse.After such earthquakes,structures must be inspected rapidly to judge whether they are safe to reoccupy.To ...Many countries throughout the world have experienced large earthquakes,which cause building damage or collapse.After such earthquakes,structures must be inspected rapidly to judge whether they are safe to reoccupy.To facilitate the inspection process,the authors previously developed a rapid building safety assessment system using sparse acceleration measurements for steel framed buildings.The proposed system modeled nonlinearity in the measurement data using a calibrated simplified lumped-mass model and convolutional neural networks(CNNs),based on which the buildinglevel damage index was estimated rapidly after earthquakes.The proposed system was validated for a nonlinear 3D numerical model of a five-story steel building,and later for a large-scale specimen of an 18-story building in Japan tested on the E-Defense shaking table.However,the applicability of the safety assessment system for reinforced concrete(RC)structures with complex hysteretic material nonlinearity has yet to be explored;the previous approach based on a simplified lumpedmass model with a Bouc-Wen hysteretic model does not accurately represent the inherent nonlinear behavior and resulting damage states of RC structures.This study extends the rapid building safety assessment system to low-rise RC moment resisting frame structures representing typical residential apartments in Japan.First,a safety classification for RC structures based on a damage index consistent with the current state of practice is defined.Then,a 3D nonlinear numerical model of a two-story moment frame structure is created.A simplified lumped-mass nonlinear model is developed and calibrated using the 3D model,incorporating the Takeda degradation model for the RC material nonlinearity.This model is used to simulate the seismic response and associated damage sensitive features(DSF)for random ground motion.The resulting database of responses is used to train a convolutional neural network(CNN)that performs rapid safety assessment.The developed system is validated using the 3D nonlinear analysis model subjected to historical earthquakes.The results indicate the applicability of the proposed system for RC structures following seismic events.展开更多
文摘电磁矢量传感器多输入多输出(electromagnetic vector sensor multiple-input multiple-output,EMVS-MIMO)雷达是一种新兴技术,可实现二维波达角(2D-DOA)估计。针对单基地稀疏阵列EMVS-MIMO雷达,提出一种基于旋转不变性信号参数估计技术ESPRIT(estimation of signal parameters via rotational invariance techniques)的降复杂度(reduced-complexity,RC)信号参数估计算法,能够实现对目标2D-DOA的快速估计。首先,对接收阵列数据进行RC处理,以消除阵列冗余数据;其次,利用ESPRIT可获得高分辨率的俯仰角估计,由于阵列的稀疏性,该估计值具有模糊性;再次,利用矢量叉积技术获得具有无模糊特性的2D-DOA;最后,利用无模糊的俯仰角估计对有周期模糊的估计进行解模糊,获得具有高分辨率、无模糊特性的俯仰角估计。该算法适用于大规模EMVS-MIMO雷达系统,且相比现有的ESPRIT-Like算法拥有更高的估计精度,通过MATLAB仿真验证了算法的有效性。
基金supported by a fellowship from Design Department of Taisei Corporation。
文摘Many countries throughout the world have experienced large earthquakes,which cause building damage or collapse.After such earthquakes,structures must be inspected rapidly to judge whether they are safe to reoccupy.To facilitate the inspection process,the authors previously developed a rapid building safety assessment system using sparse acceleration measurements for steel framed buildings.The proposed system modeled nonlinearity in the measurement data using a calibrated simplified lumped-mass model and convolutional neural networks(CNNs),based on which the buildinglevel damage index was estimated rapidly after earthquakes.The proposed system was validated for a nonlinear 3D numerical model of a five-story steel building,and later for a large-scale specimen of an 18-story building in Japan tested on the E-Defense shaking table.However,the applicability of the safety assessment system for reinforced concrete(RC)structures with complex hysteretic material nonlinearity has yet to be explored;the previous approach based on a simplified lumpedmass model with a Bouc-Wen hysteretic model does not accurately represent the inherent nonlinear behavior and resulting damage states of RC structures.This study extends the rapid building safety assessment system to low-rise RC moment resisting frame structures representing typical residential apartments in Japan.First,a safety classification for RC structures based on a damage index consistent with the current state of practice is defined.Then,a 3D nonlinear numerical model of a two-story moment frame structure is created.A simplified lumped-mass nonlinear model is developed and calibrated using the 3D model,incorporating the Takeda degradation model for the RC material nonlinearity.This model is used to simulate the seismic response and associated damage sensitive features(DSF)for random ground motion.The resulting database of responses is used to train a convolutional neural network(CNN)that performs rapid safety assessment.The developed system is validated using the 3D nonlinear analysis model subjected to historical earthquakes.The results indicate the applicability of the proposed system for RC structures following seismic events.
