Articles | Volume 17, issue 1
https://doi.org/10.5194/ms-17-207-2026
https://doi.org/10.5194/ms-17-207-2026
Research article
 | 
10 Mar 2026
Research article |  | 10 Mar 2026

Dynamic stress monitoring and analysis of cranes based on digital-twin modeling

Guoping Yan, Xiaowei Yang, Jiansheng Zhang, Qi Tao, and Yang Li

Cited articles

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Bose, A. K., Zhang, X., Maddipatla, D., Masihi, S., Panahi, M., Narakathu, B. B., Bazuin, B. J., Williams, J. D., Mitchell, M. F., and Atashbar, M. Z.: Screen-printed strain gauge for micro-strain detection applications, IEEE Sens. J., 20, 12652–12660, https://doi.org/10.1109/JSEN.2020.3002388, 2020. 
Chen, Q.: Surface crack detection of large-scale crane based on convolutional neural network, Nanjing University of Posts and Telecommunications, (04), https://doi.org/10.27251/d.cnki.gnjdc.2021.001285, 2021. 
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Short summary
This study develops a digital-twin framework for real-time structural stress monitoring of crane structures. Integrating finite-element analysis with a radial basis function surrogate model, it enables dynamic stress prediction and visualization. Experimental results show an average prediction error of 8.29% compared to simulations and 9.98% against measured data, providing a reliable technical tool for structural health monitoring and safety.
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