In determining the displacement monitoring indicators of a concrete arch dam

the traditional method did not consider the uncertainty of dam material parameters. This paper develops a new method for the determination of these indicators based on the results of stochastic parameter back analysis by combining the Bayesian inference theory

particle swarm optimization

and a long short-term memory network. We use the particle swarm optimization to optimize the long short-term memory network as the surrogate model

and derive a joint likelihood function using the displacement values measured at different monitoring points and the ones calculated by the surrogate model. With the prior distribution of material parameters given

we can use the Bayesian inference to calculate their posterior distribution. This distribution is used to calculate the dam displacement component due to water pressure; then the displacement monitoring index of the dam can be determined using displacement measurements. Analysis of engineering cases shows the Bayesian inference method gives accurate posterior results of the material parameters. The monitoring indicators

determined using the mixed method

fully utilize the results of stochastic back analysis and have better warning effects.