Implementing Structural Health Monitoring(SHM)systems with extensive sensing layouts on all civil structures is obviously expensive and unfeasible.Thus,estimating the state(condition)of dissimilar civil structures bas...Implementing Structural Health Monitoring(SHM)systems with extensive sensing layouts on all civil structures is obviously expensive and unfeasible.Thus,estimating the state(condition)of dissimilar civil structures based on the information collected from other structures is regarded as a useful and essential way.For this purpose,Structural State Translation(SST)has been recently proposed to predict the response data of civil structures based on the information acquired from a dissimilar structure.This study uses the SST methodology to translate the state of one bridge(Bridge#1)to a new state based on the knowledge acquired from a structurally dissimilar bridge(Bridge#2).Specifically,the Domain-Generalized Cycle-Generative(DGCG)model is trained in the Domain Generalization learning approach on two distinct data domains obtained from Bridge#1;the bridges have two different conditions:State-H and State-D.Then,the model is used to generalize and transfer the knowledge on Bridge#1 to Bridge#2.In doing so,DGCG translates the state of Bridge#2 to the state that the model has learned after being trained.In one scenario,Bridge#2’s State-H is translated to State-D;in another scenario,Bridge#2’s State-D is translated to State-H.The translated bridge states are then compared with the real ones via modal identifiers and mean magnitude-squared coherence(MMSC),showing that the translated states are remarkably similar to the real ones.For instance,the modes of the translated and real bridge states are similar,with the maximum frequency difference of 1.12%and the minimum correlation of 0.923 in Modal Assurance Criterion values,as well as the minimum of 0.947 in Average MMSC values.In conclusion,this study demonstrates that SST is a promising methodology for research with data scarcity and population-based structural health monitoring(PBSHM).In addition,a critical discussion about the methodology adopted in this study is also offered to address some related concerns.展开更多
The use of deep generative models(DGMs)such as variational autoencoders,autoregressive models,flow-based models,energy-based models,generative adversarial networks,and diffusion models has been advantageous in various...The use of deep generative models(DGMs)such as variational autoencoders,autoregressive models,flow-based models,energy-based models,generative adversarial networks,and diffusion models has been advantageous in various disciplines due to their high data generative skills.Using DGMs has become one of the most trending research topics in Artificial Intelligence in recent years.On the other hand,the research and development endeavors in the civil structural health monitoring(SHM)area have also been very progressive owing to the increasing use of Machine Learning techniques.As such,some of the DGMs have also been used in the civil SHM field lately.This short review communication paper aims to assist researchers in the civil SHM field in understanding the fundamentals of DGMs and,consequently,to help initiate their use for current and possible future engineering applications.On this basis,this study briefly introduces the concept and mechanism of different DGMs in a comparative fashion.While preparing this short review communication,it was observed that some DGMs had not been utilized or exploited fully in the SHM area.Accordingly,some representative studies presented in the civil SHM field that use DGMs are briefly overviewed.The study also presents a short comparative discussion on DGMs,their link to the SHM,and research directions.展开更多
基金the U.S.National Science Foundation(NSF)Division of Civil,Mechanical and Manufacturing Innovation(grant number 1463493)Transportation Research Board of The National Academies-IDEA Project 222,and National Aeronautics and Space Administration(NASA)Award No.80NSSC20K0326 for the research activities and particularly for this paper.
文摘Implementing Structural Health Monitoring(SHM)systems with extensive sensing layouts on all civil structures is obviously expensive and unfeasible.Thus,estimating the state(condition)of dissimilar civil structures based on the information collected from other structures is regarded as a useful and essential way.For this purpose,Structural State Translation(SST)has been recently proposed to predict the response data of civil structures based on the information acquired from a dissimilar structure.This study uses the SST methodology to translate the state of one bridge(Bridge#1)to a new state based on the knowledge acquired from a structurally dissimilar bridge(Bridge#2).Specifically,the Domain-Generalized Cycle-Generative(DGCG)model is trained in the Domain Generalization learning approach on two distinct data domains obtained from Bridge#1;the bridges have two different conditions:State-H and State-D.Then,the model is used to generalize and transfer the knowledge on Bridge#1 to Bridge#2.In doing so,DGCG translates the state of Bridge#2 to the state that the model has learned after being trained.In one scenario,Bridge#2’s State-H is translated to State-D;in another scenario,Bridge#2’s State-D is translated to State-H.The translated bridge states are then compared with the real ones via modal identifiers and mean magnitude-squared coherence(MMSC),showing that the translated states are remarkably similar to the real ones.For instance,the modes of the translated and real bridge states are similar,with the maximum frequency difference of 1.12%and the minimum correlation of 0.923 in Modal Assurance Criterion values,as well as the minimum of 0.947 in Average MMSC values.In conclusion,this study demonstrates that SST is a promising methodology for research with data scarcity and population-based structural health monitoring(PBSHM).In addition,a critical discussion about the methodology adopted in this study is also offered to address some related concerns.
基金the National Aeronautics and Space Administration(NASA)Award No.80NSSC20K0326 for the research activities and particularly for this paper。
文摘The use of deep generative models(DGMs)such as variational autoencoders,autoregressive models,flow-based models,energy-based models,generative adversarial networks,and diffusion models has been advantageous in various disciplines due to their high data generative skills.Using DGMs has become one of the most trending research topics in Artificial Intelligence in recent years.On the other hand,the research and development endeavors in the civil structural health monitoring(SHM)area have also been very progressive owing to the increasing use of Machine Learning techniques.As such,some of the DGMs have also been used in the civil SHM field lately.This short review communication paper aims to assist researchers in the civil SHM field in understanding the fundamentals of DGMs and,consequently,to help initiate their use for current and possible future engineering applications.On this basis,this study briefly introduces the concept and mechanism of different DGMs in a comparative fashion.While preparing this short review communication,it was observed that some DGMs had not been utilized or exploited fully in the SHM area.Accordingly,some representative studies presented in the civil SHM field that use DGMs are briefly overviewed.The study also presents a short comparative discussion on DGMs,their link to the SHM,and research directions.
