To address the challenge of real-time and global monitoring of the structural stress state of large port gantry cranes in complex working environments, this paper proposes a digital twin system framework covering the physical layer, data layer, model layer, and application layer, utilizing a container gantry crane as the case study. A multi-dimensional working condition space covering key working condition parameters such as lifting load and trolley position is designed, and a stress surrogate model based on the Radial Basis Function (RBF) neural network is constructed. This realizes a rapid mapping from low-dimensional operating parameters to high-dimensional full-field stress distributions. The surrogate model is integrated into the visualization platform, achieving real-time dynamic rendering and threshold exceedance warning of the stress of the key structures of the crane. The results show that the constructed surrogate model ensures the prediction accuracy (R2 > 0.94) and achieves millisecond-level calculation response, demonstrating good real-time performance and reliability. It provides a reference for the digital monitoring of large-scale equipment.
Liu et al. (Wed,) studied this question.