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
The performance of different nonlinear modelling strategies to simulate the response of RC columns subjected to axial load combined with cyclic biaxial horizontal loading is compared. The models studied are classified...The performance of different nonlinear modelling strategies to simulate the response of RC columns subjected to axial load combined with cyclic biaxial horizontal loading is compared. The models studied are classified into two categories according to the nonlinearity distribution assumed in the elements： lumped-plasticity and distributed inelasticity. For this study, results of tests on 24 columns subjected to cyclic uniaxial and biaxial lateral displacements were numerically reproduced. The analyses show that the global envelope response is satisfactorily represented with the three modelling strategies, but significant differences were found in the strength degradation for higher drift demands and energy dissipation.展开更多
In order to fast analyze the aircraft Radar Cross Section(RCS) and accurately reduce it with Radar Absorbing Materials(RAM), a comprehensive analysis method based on Higher-Order Method of Moments(HOMOM), termed Local...In order to fast analyze the aircraft Radar Cross Section(RCS) and accurately reduce it with Radar Absorbing Materials(RAM), a comprehensive analysis method based on Higher-Order Method of Moments(HOMOM), termed Locally Coating Method(LCM), is proposed in this paper. There are two steps to fast analyze coatings for RCS reduction in this method: analyze the RCS of various parts before coating the aircraft;model a coating over the aircraft and analyze the wave absorbing effect of it. The aircraft RCS is calculated as a whole but analyzed in various parts by LCM, and thus the RCS contribution of different parts can be compared without disturbing the current continuity. A model expansion algorithm is also presented in LCM to model absorption coatings on specified aircraft parts for later stage RCS calculation of the coated aircraft.展开更多
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.
基金Financial support provided by "FCT - Fundao para a Ciência e Tecnologia,"Portugal,through the research project PTDC/ECM/102221/2008
文摘The performance of different nonlinear modelling strategies to simulate the response of RC columns subjected to axial load combined with cyclic biaxial horizontal loading is compared. The models studied are classified into two categories according to the nonlinearity distribution assumed in the elements： lumped-plasticity and distributed inelasticity. For this study, results of tests on 24 columns subjected to cyclic uniaxial and biaxial lateral displacements were numerically reproduced. The analyses show that the global envelope response is satisfactorily represented with the three modelling strategies, but significant differences were found in the strength degradation for higher drift demands and energy dissipation.
基金supported by the National Key Research and Development Program of China (No. 2017YFB0202102),the National Key Research and Development Program of China (No. 2016YFE0121600)the China Postdoctoral Science Foundation funded project (No. 2017M613068)+2 种基金the National High Technology Research and Development Program of China (863 Program) (No. 2014AA01A302)the Key Research and Development Program of Shandong Province, China (No. 2015GGX101028)the Special Program for Applied Research on Super Computation of the NSFC (National Natural Science Foundation of China)-Guangdong Joint Fund, China (the second phase) (No. U1501501)
文摘In order to fast analyze the aircraft Radar Cross Section(RCS) and accurately reduce it with Radar Absorbing Materials(RAM), a comprehensive analysis method based on Higher-Order Method of Moments(HOMOM), termed Locally Coating Method(LCM), is proposed in this paper. There are two steps to fast analyze coatings for RCS reduction in this method: analyze the RCS of various parts before coating the aircraft;model a coating over the aircraft and analyze the wave absorbing effect of it. The aircraft RCS is calculated as a whole but analyzed in various parts by LCM, and thus the RCS contribution of different parts can be compared without disturbing the current continuity. A model expansion algorithm is also presented in LCM to model absorption coatings on specified aircraft parts for later stage RCS calculation of the coated aircraft.
