A double-layer progressive architecture-based surrogate model for efficiency analysis of spiral shaft in shield machine

Abstract

To address existing methodological limitations, a novel method for constructing a simulation surrogate model for a spiral shaft is proposed based on a double-layer progressive architecture, which consists of two steps. Firstly, the Goal-oriented autoencoder (GOAE)-classifier model discriminates the feasibility of the samples, ensuring accuracy through differential feature extraction by GOAE. Then, the Self-attention Artificial Neural Network (Self-attention ANN) weight allocation model assigns adaptive weights to surrogate models under different working conditions, which implements a dynamic combination of sub-model outputs and improves the model accuracy. Validation through a dataset generated via Latin hypercube sampling (LHS) and simulation affirms the efficacy, demonstrating fast and accurate simulation. The results underscore its potential for collaborative optimization. In summary, the proposed method has obvious advantages and can be extended to other fields to meet the needs of engineering.