Variations between earthquakes result in many factors that influence post-earthquake building damage(e.g.,ground motion parameters,building structure,site information,and quality of construction).Consequently,it is ne...Variations between earthquakes result in many factors that influence post-earthquake building damage(e.g.,ground motion parameters,building structure,site information,and quality of construction).Consequently,it is necessary to develop an appropriate building damage-rate estimation model.The building damage survey data were recorded and constructed into files by the Architecture and Building Research Institute(ABRI),Taiwan for the 1999 Chi-Chi earthquake in the Nantou region as a basis for developing a building damage rate estimation model by applying fuzzy theory to express the fragility curves of buildings as a membership function.Empirical verification was performed using post-earthquake building damage data in the Taichung city that suffered relatively severe damage.Results indicate that fuzzy theory can be applied to predict building damage rates and that the estimated results are similar to actual disaster figures.Prediction of disaster damage using building damage rates can provide a reference for immediate disaster response during earthquakes and for regular disaster prevention and rescue planning.展开更多
The contribution rate of equipment system-of-systems architecture(ESoSA)is an important index to evaluate the equipment update,development,and architecture optimization.Since the traditional ESoSA contribution rate ev...The contribution rate of equipment system-of-systems architecture(ESoSA)is an important index to evaluate the equipment update,development,and architecture optimization.Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems(ESoS),and the Bayesian network is an effective tool to solve the uncertain information,a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network(FBN)is proposed.Firstly,based on the operation loop theory,an ESoSA is constructed considering three aspects:reconnaissance equipment,decision equipment,and strike equipment.Next,the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information.Furthermore,the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA,and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established.Finally,the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA.Compared with traditional methods,the evaluation method based on FBN takes various failure states of equipment into consideration,is free of acquiring accurate probability of traditional equipment failure,and models the uncertainty of the relationship between equipment.The proposed method not only supplements and improves the ESoSA contribution rate assessment method,but also broadens the application scope of the Bayesian network.展开更多
基金Project(93-2625-Z-027-006)supported by the National Science Council of Taipei,China
文摘Variations between earthquakes result in many factors that influence post-earthquake building damage(e.g.,ground motion parameters,building structure,site information,and quality of construction).Consequently,it is necessary to develop an appropriate building damage-rate estimation model.The building damage survey data were recorded and constructed into files by the Architecture and Building Research Institute(ABRI),Taiwan for the 1999 Chi-Chi earthquake in the Nantou region as a basis for developing a building damage rate estimation model by applying fuzzy theory to express the fragility curves of buildings as a membership function.Empirical verification was performed using post-earthquake building damage data in the Taichung city that suffered relatively severe damage.Results indicate that fuzzy theory can be applied to predict building damage rates and that the estimated results are similar to actual disaster figures.Prediction of disaster damage using building damage rates can provide a reference for immediate disaster response during earthquakes and for regular disaster prevention and rescue planning.
基金supported by the National Key Research and Development Project(2018YFB1700802)the National Natural Science Foundation of China(72071206)the Science and Technology Innovation Plan of Hunan Province(2020RC4046).
文摘The contribution rate of equipment system-of-systems architecture(ESoSA)is an important index to evaluate the equipment update,development,and architecture optimization.Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems(ESoS),and the Bayesian network is an effective tool to solve the uncertain information,a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network(FBN)is proposed.Firstly,based on the operation loop theory,an ESoSA is constructed considering three aspects:reconnaissance equipment,decision equipment,and strike equipment.Next,the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information.Furthermore,the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA,and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established.Finally,the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA.Compared with traditional methods,the evaluation method based on FBN takes various failure states of equipment into consideration,is free of acquiring accurate probability of traditional equipment failure,and models the uncertainty of the relationship between equipment.The proposed method not only supplements and improves the ESoSA contribution rate assessment method,but also broadens the application scope of the Bayesian network.
