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Title: Multi-objective optimisation of shield synchronous grouting materials: a synergistic architecture integrating intelligent algorithms and convolutional neural network

Authors: Aijuan Shi; Kun Li; Hui Yang

Addresses: Intelligent Manufacturing College, Anhui Wenda University of Information Engineering, Hefei, 231201, China ' Intelligent Manufacturing College, Anhui Wenda University of Information Engineering, Hefei, 231201, China ' Intelligent Manufacturing College, Anhui Wenda University of Information Engineering, Hefei, 231201, China

Abstract: This study addresses the multi-objective optimisation problem involved in designing shield synchronous grouting material ratios by proposing a collaborative architecture that integrates an improved multi-objective grey wolf optimisation algorithm and a one-dimensional convolutional neural network. A high-precision surrogate model constructed by the one-dimensional convolutional neural network accurately predicts material strength, achieving a test set R² of 0.961 and a root mean square error of 3.12 MPa. The improved multi-objective grey wolf optimisation algorithm is then applied to simultaneously optimise both material strength and cost. Experimental results indicate that the proposed method outperforms comparison algorithms across multiple performance indicators, including inverted generational distance (0.038), hypervolume (0.752), and spacing (0.015). These outcomes confirm the effectiveness of the architecture in enhancing optimisation efficiency and solution set quality, offering a practical and intelligent approach to grouting material design.

Keywords: shield synchronous grouting; multi-objective optimisation; convolutional neural network; CNN; grey wolf optimisation algorithm; material equipment.

DOI: 10.1504/IJICT.2026.151559

International Journal of Information and Communication Technology, 2026 Vol.27 No.6, pp.45 - 65

Received: 20 Oct 2025
Accepted: 16 Nov 2025
Published online: 06 Feb 2026 *