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Research on Application of Weighted Sampling Bayesian Network Parameter Learning in Bridge Condition Assessment

EM algorithm is a traditional Bayesian network parameter learning method. In practical applications, due to the complex structure of the network and large data deviations, the computational complexity problem is the major factor limiting its development. Aiming at the efficiency of EM algorithm, this paper proposes an improved algorithm of sampling EM with measurement index completion. The algorithm first modifies the iterative judgment of the EM algorithm, and proposes a judgment method for setting a measurement index for each missing sample to improve the calculation efficiency; meanwhile, Gibbs sampling is introduced to optimize the sample completion calculation, so as to reduce the overall computational complexity of the algorithm. Finally, the algorithm is used to evaluate the health status of Wenhui Bridge in Liuzhou to prove the practicability of the algorithm.