Title: Mechanical performance testing and GA-BP model prediction of open-hole rubber bearings

Authors: Shaowei Jiang; Lixing Lu; Haode Cheng; Yingxiong Wu

Addresses: China Construction Fourth Engineering Divion Corp., Ltd., Guangzhou 510630, China ' School of Civil Engineering, Fuzhou University, Fujian 350108, China ' School of Civil Engineering, Fuzhou University, Fujian 350108, China ' School of Civil Engineering, Fuzhou University, Fujian 350108, China

Abstract: Open-hole rubber bearings exhibit heightened sensitivity to external loads due to their internal perforated structure. This paper systematically investigates the mechanical behaviour of open-hole rubber bearings through experimental analysis, numerical simulation, theoretical model refinement, and the development of a GA-BP-based predictive model. A rubber bearing with an open-hole ratio of 6.67% was designed, and vertical compression and horizontal shear tests were conducted. Through detailed numerical simulations, the effects of varying the open-hole ratio on the mechanical properties of the rubber bearings and the performance of stiffness prediction models were thoroughly analysed. The research indicates that the vertical hysteresis curves of open-hole rubber bearings are well-defined and exhibit a distinct bilinear characteristic in the skeleton curves. The open-hole design not only affects the overall stress distribution on the steel plates but also alters the distribution pattern of stress concentration zones. Vertical stiffness gradually decreases with an increase in the open-hole ratio, while horizontal stiffness shows a complex trend due to varying stress and shear strain conditions. Furthermore, the GA-BP neural network predictive model for stiffness of open-hole rubber bearings demonstrates strong generalisation ability and high prediction accuracy.

Keywords: open-hole rubber bearing; plasticity ratio; horizontal stiffness; vertical stiffness; GA-BP neural network.

DOI: 10.1504/IJSTRUCTE.2026.155428

International Journal of Structural Engineering, 2026 Vol.16 No.3, pp.231 - 254

Received: 26 Aug 2025
Accepted: 03 Dec 2025

Published online: 31 Jul 2026 *

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