Intelligent inference of structural vulnerability using Bayesian networks
In order to realize the intelligent inference of structural vulnerability,Bayesian networks(BNs)have been adopted for reconstructing the analysis system of a truss structure.Firstly,the external load combination,the truss system and its members are defined as the top parent nodes,the middle nodes and the bottom child nodes of the network,respectively.These nodes are connected by some directed edges representing the causality between them.Therefore,the BN topology of the truss structure is defined.Secondly,serious damage is suggested to replace the commonly-used assumption of conceptual removal in vulnerability analysis.Based on the uncertainty distributions of the parameters of members and external loads,the samples are randomly drawn from the probability distributions for learning the conditional probability tables between the different nodes,thereby realizing the BN establishment.Thirdly,the observed state of a specific member is used as the evidence into the established BN for synchronously inferring the state probabilities of the other members,based on which the member importance coefficient is calculated.The sum of all the importance coefficients is further defined as the system vulnerability index.Finally,a member vulnerability index is proposed to predict the most probable failure path of the truss system.The numerical and experimental examples have demonstrated that the proposed method can effectively evaluate the importance of each member within a truss system.The inferred failure path accords well with the experimental observation.The estimated value of the system vulnerability index of the experimental truss is far less than the number of members,and thus,it indicates the low possibility of progressive collapse of the system due to the damaged member.
structural engineeringBayesian networksmember importance coefficientsystem vulnerability indexmember vulnerability index
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