Research on Surrogate Model of Dam Structural Behavior for Multi‐Output Problem

ABSTRACT The establishment efficiency of the surrogate model is often affected by the multi‐output problem during the establishment process. It is an urgent issue to solve how to establish a multi‐output joint surrogate model more quickly while ensuring a certain level of accuracy. In recent years, the advancement of artificial intelligence technology has provided a more efficient measure for establishing a multi‐output joint surrogate model. Multilayer perceptron (MLP) is one of the most widely employed deep learning models and is commonly used to establish the surrogate model. How to establish a reasonable MLP surrogate model is the presumption and basis of establishing a surrogate model. Based on a review of the pertinent literature pertaining to MLP as a surrogate model, this paper examines the techniques and methods of MLP establishment. This paper proposes a framework for the establishment of a multi‐output MLP joint surrogate model based on the aforementioned techniques and methods, as well as the existing problems associated with its establishment. On the basis of this framework, a surrogate model for the behavior of dam structural is developed. By confirming the model evaluation index, the performance of the surrogate model for dam structural behavior can be determined to be satisfactory. In addition, the feasibility of this framework is demonstrated by comparing it with independent models that establish surrogate models one by one for multi‐output.