A stochastic model updating method is proposed by combining the Cokriging surrogate model technique with the single objective function. The method transforms the uncertainty model updating problem into a simple updating problem of the statistical characteristics of the parameters to be updated

which can effectively alleviate the high computational cost problem caused by the stepwise and multi-objective updating while ensuring the updating accuracy. Firstly

assuming that the parameters to be updated and the corresponding responses obey Gaussian distribution

the Latin hypercube sampling is used to obtain the training set samples

and the Cokriging model satisfying the accuracy requirement is constructed to replace the complex finite element model in the iterative calculation. Then

the weighted residual objective function between the statistical characteristics of the finite element model calculated responses and the statistical characteristics of the test responses is established

and the coyote optimization algorithm is introduced to minimize the single objective function to obtain the statistical characteristics of the parameters to be updated. Finally

the feasibility of the proposed method is verified by the two-dimensional and the three-dimensional truss structures.