Buildings constructed prior to the implementation of seismic design standards or those built based on lower standards are susceptible to earthquake risks,resulting in substantial loss of life and property during an im...Buildings constructed prior to the implementation of seismic design standards or those built based on lower standards are susceptible to earthquake risks,resulting in substantial loss of life and property during an imminent earthquake.Although conventional rapid visual screening(RVS)methods have been extensively developed,both nationally and in the literature,they have limitations in accurately determining the vulnerability of buildings.Additionally,RVS methods developed on the basis of a single algorithm have limitations.Therefore,this study extends the existing body of work by integrating multiple AI algorithms,including fuzzy logic,machine learning,and neural networks,in the context of building damage data from the 2015 Gorkha earthquake,overcoming the limitations of previous studies by introducing an automated AI-based RVS methodology that enhances accuracy,transparency,and adaptability.The newly developed RVS method demonstrates an accuracy rate of 45.89%for testing in the three-class classification,while also delivering promising results in the two-class classification,with an accuracy rate of 60%,surpassing both conventional RVS methods and the baseline accuracy rate.展开更多
Tuned mass dampers (TMD) are well known as one of the most widely adopted devices in vibration control passive strategies. In the past few decades,many methods have been developed to find the optimal parameters of a T...Tuned mass dampers (TMD) are well known as one of the most widely adopted devices in vibration control passive strategies. In the past few decades,many methods have been developed to find the optimal parameters of a TMD installed on a structure and subjected to a random base excitation process,but most of them are usually based on an implicit assumption that all of the structural parameters are deterministic. However,in many real cases this simplification is unacceptable,so robust optimal design criteria becomes aviable alternative to better support engineers in the design process. In Robust Design Optimization (RDO) approaches,indeed the solution must be able to not only minimize the performance but also to limitits variation induced by uncertainty. Most of the currently available RDO methods are based on a probabilistic description of the model uncertainty,even if in many cases they are not able to explicitly include the influence of all the possible sources of uncertainties. Therefore,in this study,a fuzzy version of the robust TMD design optimization problem is proposed. The consistency of the fuzzy approach is studied with respect to the available non-probabilistic formulations reported in the literature and an application to an example of a robust design of a linear TMD subjected to base random vibrations in the presence of fuzzy uncertainties. The results show that the proposed fuzzy-based approach is able to give a set of optimal solutions both in terms of structural efficiency and sensitivity to mechanical and environmental uncertainties.展开更多
基金Open access funding provided by Széchenyi IstvÃn University.
文摘Buildings constructed prior to the implementation of seismic design standards or those built based on lower standards are susceptible to earthquake risks,resulting in substantial loss of life and property during an imminent earthquake.Although conventional rapid visual screening(RVS)methods have been extensively developed,both nationally and in the literature,they have limitations in accurately determining the vulnerability of buildings.Additionally,RVS methods developed on the basis of a single algorithm have limitations.Therefore,this study extends the existing body of work by integrating multiple AI algorithms,including fuzzy logic,machine learning,and neural networks,in the context of building damage data from the 2015 Gorkha earthquake,overcoming the limitations of previous studies by introducing an automated AI-based RVS methodology that enhances accuracy,transparency,and adaptability.The newly developed RVS method demonstrates an accuracy rate of 45.89%for testing in the three-class classification,while also delivering promising results in the two-class classification,with an accuracy rate of 60%,surpassing both conventional RVS methods and the baseline accuracy rate.
文摘Tuned mass dampers (TMD) are well known as one of the most widely adopted devices in vibration control passive strategies. In the past few decades,many methods have been developed to find the optimal parameters of a TMD installed on a structure and subjected to a random base excitation process,but most of them are usually based on an implicit assumption that all of the structural parameters are deterministic. However,in many real cases this simplification is unacceptable,so robust optimal design criteria becomes aviable alternative to better support engineers in the design process. In Robust Design Optimization (RDO) approaches,indeed the solution must be able to not only minimize the performance but also to limitits variation induced by uncertainty. Most of the currently available RDO methods are based on a probabilistic description of the model uncertainty,even if in many cases they are not able to explicitly include the influence of all the possible sources of uncertainties. Therefore,in this study,a fuzzy version of the robust TMD design optimization problem is proposed. The consistency of the fuzzy approach is studied with respect to the available non-probabilistic formulations reported in the literature and an application to an example of a robust design of a linear TMD subjected to base random vibrations in the presence of fuzzy uncertainties. The results show that the proposed fuzzy-based approach is able to give a set of optimal solutions both in terms of structural efficiency and sensitivity to mechanical and environmental uncertainties.
