Dynamic parameter inverse analysis of concrete dams based on Jaya algorithm with Gaussian processes surrogate model

A dynamic parameter inverse analysis process for concrete dams based on Gaussian process regression and Jaya algorithm is presented. Gaussian process regression is used to establish a response surface representing the relationship between dynamic elastic modulus and modal parameters (natural frequency and mode shape). The Jaya algorithm is applied for dynamic parameter identification by minimizing the objective function. To verify the performance of the proposed method, we consider a concrete single buttress dam and a hyperbolic concrete arch dam as numerical examples. Numerical results show that Gaussian process regression can significantly improve the parameter identification efficiency without compromising on accuracy. Furthermore, the Jaya al-gorithm is compared with particle swarm optimization algorithm and genetic algorithm; the results show that the Jaya algorithm is promising in parameter recognition.

Automated summary

This study presents a dynamic parameter inverse analysis process for concrete dams based on Gaussian process regression and Jaya algorithm, and verifies the performance of this method through numerical examples. The results show that Gaussian process regression can significantly improve parameter identification efficiency, and the Jaya algorithm shows promise in parameter recognition.