Resistance to unexpected disasters and rapid post-disaster recovery(i.e.,disaster resilience)of cities are extremely necessary owing to the concentrated risk of urbanization.Resilience quantification can adequately re...Resistance to unexpected disasters and rapid post-disaster recovery(i.e.,disaster resilience)of cities are extremely necessary owing to the concentrated risk of urbanization.Resilience quantification can adequately reflect the capacity of a city to withstand disasters.Many existing studies have focused on and proposed several frameworks on the quantitative measures of disaster resilience,and the corresponding research objects include different types of disasters(e.g.,earthquake,hurricane,flood,and fire),various domains(e.g.,engineering,social,and economic),and multiple levels(e.g.,city,community,and building).Among these research objects,studies on seismic resilience in civil engineering are relatively comprehensive.Specifically,studies on resilience in civil engineering have paid significant attention to the dynamics of engineering facilities and the engineering-related social and economic functions,including city-scale engineering,social,and economic functionalities,and essential functionalities of building,transportation,lifeline,and nonphysical subsystems of a city.Consequently,based on the review of resilience studies carried out in recent years,the framework and specifications for the quantification of disaster resilience of civil engineering systems subjected to earthquakes and other unexpected disasters are elaborated.Methods of disaster resilience assessment of cities and the corresponding subsystems are discussed.Furthermore,several case studies are reviewed,and resilience limit-state analyses of communities and buildings are performed.展开更多
Various structural defects deteriorate tunnel operation status and threaten public safety.Current tunnel inspection methods face problems of low efficiency,high equipment expense,and difficult data management.Combinin...Various structural defects deteriorate tunnel operation status and threaten public safety.Current tunnel inspection methods face problems of low efficiency,high equipment expense,and difficult data management.Combining the deep learning model and the 3D reconstruction method based on structure from motion(SfM),this paper proposes a novel SfM-Deep learning method for tunnel inspection.The high-quality 3D tunnel model is constructed by using images taken every 1 m along the longitudinal direction.The instance segmentation of leakage in longitudinal images is realized using the mask region-based convolutional neural network deep learning model.The SfM-Deep learning method projects the texture of the images after defect recognition to the 3D model and realizes the visualization of leakage defects.By projecting the model to the design cylindrical surface and expanding it,the tunnel leakage area is quantified.Through its practical application in a Shanghai metro shield tunnel,the reliability of the proposed method was verified.The novel SfM-Deep learning method can help engineers efficiently carry out intelligent tunnel detection.展开更多
This paper investigates the spatial effect of environmental regulation measures on the upgrading of industrial structure in the integrated development strategy of Beijing-Tianjin-Hebei region and its surrounding areas...This paper investigates the spatial effect of environmental regulation measures on the upgrading of industrial structure in the integrated development strategy of Beijing-Tianjin-Hebei region and its surrounding areas.In order to reflect the effect of the environmental regulation and different regulation measures more truly,this paper constructs indices of the environmental regulation measures through the results of policy texts quantification.On the basis of the previous research,this paper divides environmental regulation into the following types:personnel and administrative measures of command and control;market-oriented fiscal,taxation,financial and other economic measures;guidance measures.Spatial panel regression results show that administrative measures of command control and market-oriented fiscal measures have a significant role in promoting regional industrial structure upgrading,but not conducive to the advancement of the industrial structure of adjacent areas.Their roles in promoting and inhibition are counteracted,which causes the total effect of industrial structure upgrading of Beijing-Tianjin-Hebei and the surrounding areas are not significant.Personnel measures,financial measures,other economic measures and guidance measures do not have the short-term effect and spatial effect on the upgrading of industrial structure.展开更多
High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated wi...High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.展开更多
基金国家社科基金青年项目"形式语义学与语义地图理论双重视角下的汉语量化现象研究"(16CYY001)香港研究资助局GRF项目"Cross-linguistic investigation into universal quantification and other related notions and their semantic map"(CUHK11601315)北京语言大学形式语言学发展研究基金(项目号451149102)的支持
基金The authors are grateful for the financial support from the National Natural Science Founda-tion of China(No.U1709212)the Tencent Foundation through the XPLORER PRIZE.
文摘Resistance to unexpected disasters and rapid post-disaster recovery(i.e.,disaster resilience)of cities are extremely necessary owing to the concentrated risk of urbanization.Resilience quantification can adequately reflect the capacity of a city to withstand disasters.Many existing studies have focused on and proposed several frameworks on the quantitative measures of disaster resilience,and the corresponding research objects include different types of disasters(e.g.,earthquake,hurricane,flood,and fire),various domains(e.g.,engineering,social,and economic),and multiple levels(e.g.,city,community,and building).Among these research objects,studies on seismic resilience in civil engineering are relatively comprehensive.Specifically,studies on resilience in civil engineering have paid significant attention to the dynamics of engineering facilities and the engineering-related social and economic functions,including city-scale engineering,social,and economic functionalities,and essential functionalities of building,transportation,lifeline,and nonphysical subsystems of a city.Consequently,based on the review of resilience studies carried out in recent years,the framework and specifications for the quantification of disaster resilience of civil engineering systems subjected to earthquakes and other unexpected disasters are elaborated.Methods of disaster resilience assessment of cities and the corresponding subsystems are discussed.Furthermore,several case studies are reviewed,and resilience limit-state analyses of communities and buildings are performed.
基金supported by the Key Field Science and Technology Project of Yunnan Province(Grant No.202002AC080002)the National Natural-Science Foundation of China(Grant No.52078377).
文摘Various structural defects deteriorate tunnel operation status and threaten public safety.Current tunnel inspection methods face problems of low efficiency,high equipment expense,and difficult data management.Combining the deep learning model and the 3D reconstruction method based on structure from motion(SfM),this paper proposes a novel SfM-Deep learning method for tunnel inspection.The high-quality 3D tunnel model is constructed by using images taken every 1 m along the longitudinal direction.The instance segmentation of leakage in longitudinal images is realized using the mask region-based convolutional neural network deep learning model.The SfM-Deep learning method projects the texture of the images after defect recognition to the 3D model and realizes the visualization of leakage defects.By projecting the model to the design cylindrical surface and expanding it,the tunnel leakage area is quantified.Through its practical application in a Shanghai metro shield tunnel,the reliability of the proposed method was verified.The novel SfM-Deep learning method can help engineers efficiently carry out intelligent tunnel detection.
基金We are grateful to the financial support from the National Natural Science Foundation of China[Grant number:71874074,Grant number:71433005,Grant number:71804063]Humanities and Social Science Fund of Ministry of Education of China[Grant number:18YJC630208,Grant number:19YJC810007]。
文摘This paper investigates the spatial effect of environmental regulation measures on the upgrading of industrial structure in the integrated development strategy of Beijing-Tianjin-Hebei region and its surrounding areas.In order to reflect the effect of the environmental regulation and different regulation measures more truly,this paper constructs indices of the environmental regulation measures through the results of policy texts quantification.On the basis of the previous research,this paper divides environmental regulation into the following types:personnel and administrative measures of command and control;market-oriented fiscal,taxation,financial and other economic measures;guidance measures.Spatial panel regression results show that administrative measures of command control and market-oriented fiscal measures have a significant role in promoting regional industrial structure upgrading,but not conducive to the advancement of the industrial structure of adjacent areas.Their roles in promoting and inhibition are counteracted,which causes the total effect of industrial structure upgrading of Beijing-Tianjin-Hebei and the surrounding areas are not significant.Personnel measures,financial measures,other economic measures and guidance measures do not have the short-term effect and spatial effect on the upgrading of industrial structure.
基金supported by the Shanghai Rising-Star Program (20QA1406800)the National Natural Science Foundation of China (22072091,91745102,92045301)。
文摘High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.
