g

Patch-Loading Resistance of High-Strength Stainless Steel Plate Girders with Web Openings

Authors

  • Akari Yamaguchi

    Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Australia
    Author

  • Boyce Diane

    Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Australia
    Author

  • Oceane Picard

    Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Australia
    Author

DOI:

https://doi.org/10.71465/jgs47

Keywords:

Patch Loading, High-Strength Stainless Steel, Web Openings, Plate Girders, Structural Stability

Abstract

The structural integrity of plate girders subjected to concentrated transverse forces, commonly referred to as patch loading, is a critical design consideration in modern civil and structural engineering. The introduction of web openings, which are frequently required for the passage of utility services or for maintenance inspections, significantly disrupts the stress field within the girder web and exacerbates the vulnerability to local instability phenomena. Concurrently, the increasing utilization of high-strength stainless steel in infrastructure projects demands a comprehensive understanding of its unique nonlinear material characteristics, specifically its pronounced strain-hardening behavior and lack of a distinct yield plateau. This paper presents an extensive investigation into the patch-loading resistance of high-strength stainless steel plate girders featuring circular and rectangular web openings. A rigorous experimental program is meticulously detailed, encompassing material characterization, geometric imperfection measurements, and full-scale ultimate load testing. The physical experiments are complemented by highly sophisticated, validated non-linear finite element models, which incorporate complex material constitutive laws and detailed contact mechanics. Through an exhaustive parametric study, the influence of varying opening dimensions, opening locations relative to the applied load, and panel aspect ratios is quantified. The results indicate that the presence of openings fundamentally alters the failure mechanism from global web buckling to localized crippling near the opening edge. Furthermore, an evaluation of existing design provisions reveals their inadequacy when applied to high-strength stainless steel components with web discontinuities.

References

1. Fan, J., Liang, W., & Zhang, W. Q. (2025). SARNet: A Spike-Aware consecutive validation Framework for Accurate Remaining Useful Life Prediction. arXiv preprint arXiv:2510.22955.

2. Hawi, P., Yao, Z., Aitharaju, V., Mahishi, J., & Ghanem, R. (2024, October). Reliability-Based Design and Certification of Hybrid Composites. In American Society for Composites (ASC) Annual Technical Conference (pp. 117-131). Cham: Springer Nature Switzerland.

3. Qi, Z., Wang, S., Zhang, W., & Huang, Q. (2024). Uncertainty-boosted robust video activity anticipation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12), 7775-7792.

4. Liu, Y., Feng, W., Liu, Z., Huang, S., & Lv, J. (2025). Aligning information capacity between vision and language via dense-to-sparse feature distillation for image-text matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 21679-21688).

5. Kashif, M., Khan, A. A., Sun, H., Yousaf, J., Shah, M. I. A., Hussain, S., ... & Ahmad, T. (2024). Synthesis and characterization of Fe-doped CuO nanoparticles: Catalytic efficiency in crystal violet dye degradation and exploration of electrical properties. Brazilian Journal of Science, 3(8), 1-18.

6. Huang, J., & Sandeep, H. (2025, October). RESEARCH ON REINFORCEMENT LEARNING FOR TRUSTWORTHY CLIMATE POLICY SIMULATION AND APPLICATION. In The 7th International scientific and practical conference “Development of modern scientific technologies in the era of globalization”(October 14-17, 2025) Berlin, Germany. International Science Group. 2025. 237 p. (p. 32).

7. Yao, Y., Zhang, W., & Li, M. (2026). Cloud-Edge Federated Incremental Learning Framework for PMSM Efficiency Optimization with Lightweight CNN-LSTM Models and OTA Differential Deployment. Journal of Computer Science and Frontier Technologies, 3(1), 98-111.

8. Gu, D., Sun, Y., Hu, J., Lu, R., & Pan, J. (2026). Rheology-and fracture-guided design of lightweight spray-applied engineered cementitious composites achieving structural-grade strength and high tensile ductility. Journal of Building Engineering, 116100.

9. Xing, J., & Xu, J. (2022). An improved convolutional neural network for recognition of incipient faults. IEEE Sensors Journal, 22(16), 16314-16322.

10. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems.

11. Li, K., Ren, Y., Fan, D., Liu, L., Wang, Z., & Ma, Z. (2018). Enhance GO methodology for reliability analysis of the closed-loop system using Cyclic Bayesian Networks. Mechanical Systems and Signal Processing, 113, 237-252.

12. Gu, D., Zhu, Y., Luković, M., & Pan, J. (2026). Arch action and beam action in reinforced engineered cementitious composite (ECC) beams subjected to shear. Engineering Structures, 357, 122468.

13. Li, Y., Liao, X., Tang, S., Wang, Q., Lin, H., Yu, X., ... & Zhong, T. (2025). Potential Therapeutic Targets for Androgenetic Alopecia (AGA) in Obese Individuals as Revealed by a Gut Microbiome Analysis: A Mendelian Randomization Study. Nutrients, 17(11), 1892.

14. Mo, J., Uy, B., Li, D., Thai, H. T., & Wang, Y. (2022). Behaviour and design of composite walls under axial compression. Journal of Constructional Steel Research, 199, 107635.

15. Wang, X., Wang, P., Jiang, W., Wu, F., Kiani, M., & Arefi, M. (2021). Mathematical and computer simulation for Electro-Magneto-Thermo-Elastic Buckling of the Porous Nano system. Structural Engineering and Mechanics, 80(2), 231-242.

16. Wang, Y., Uy, B., Li, D., Thai, H. T., Mo, J., & Khan, M. (2023). Behaviour and design of coupled steel-concrete composite wall-frame structures. Journal of Constructional Steel Research, 208, 107984.

17. Xing, J., & Xu, J. (2022). An improved incipient fault diagnosis method of bearing damage based on hierarchical multi-scale reverse dispersion entropy. Entropy, 24(6), 770.

18. Gu, D., Xu, H., Huang, Y., Zhu, Y., Pan, J., & Luković, M. (2024). Shear crack kinematics in reinforced engineered cementitious composite (ECC) beams. Case Studies in Construction Materials, 21, e03587.

19. Wang, S., Wei, G., Gao, M., & Fan, Y. (2023). Investigating the mechanism of urbanization on the net primary productivity of vegetation in the Yangtze River Economic Belt: a comprehensive analysis from global and local effects. Frontiers in Ecology and Evolution, 11, 1231487.

20. Zhang, W., Luo, M., & Chen, Z. (2024, October). Hybrid Forecasting: ML Predictions of Lake-Effect Regional Extreme Precipitations. In 2024 7th International Conference on Universal Village (UV) (pp. 1-11). IEEE.

21. Chen, Q., Yu, Z., & Li, B. (2025, March). Image-assisted seismic damage evaluation for reinforced concrete interior beam-column joints. In Structures (Vol. 73, p. 108349). Elsevier.

22. Gu, D., Jiang, B., Lin, J., Xu, L., & Pan, J. (2025). Shear behaviour of steel-headed stud connectors in engineered cementitious composite (ECC) bridge deck under positive and negative moments. Engineering Structures, 324, 119316.

23. Wei, G., Zhang, W., Bi, M., Sun, P., Li, S., Ouyang, X., ... & Tian, X. (2024). Trade-offs and synergies pattern evolution of ecosystem structure-resilience-activity-services (SRAS) in the Belt and Road Initiative region. Resources, Conservation and Recycling, 211, 107883.

24. Zhang, X., Chen, Z., Becker, B., Shan, T., Chen, T., & Gong, Q. (2025). Abnormal developmental of hippocampal subfields and amygdalar subnuclei volumes in young adults with heavy cannabis use: A three-year longitudinal study. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 136, 111156.

25. Li, C., Xu, J., & Xing, J. (2023). A frequency feature extraction method based on convolutional neural network for recognition of incipient fault. IEEE Sensors Journal, 24(1), 564-572.

26. Mo, J., Uy, B., Li, D., Thai, H. T., & Wang, Y. (2023). Behaviour and design of composite walls under lateral loading and combined loading. Journal of Constructional Steel Research, 210, 108087.

27. Chen, Q., Yu, Z., & Li, B. (2024). Image-based assessment of seismic damage in RC exterior beam-column joints. Journal of Building Engineering, 97, 110971.

28. Shan, T., Wenner, C. E., Xu, C., Duan, Z., & Maddox, R. K. (2022). Speech-in-noise comprehension is improved when viewing a deep-neural-network-generated talking face. Trends in Hearing, 26, 23312165221136934.

29. Wang, C., Song, R., Muller, R., Monteuuis, J. P., Celik, Z. B., Petit, J., ... & Li, M. (2026, March). Cp-freezer: Latency attacks against vehicular cooperative perception. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, No. 2, pp. 1114-1122).

30. Wang, S., Sun, P., Sun, F., Jiang, S., Zhang, Z., & Wei, G. (2021). The direct and spillover effect of multi-dimensional urbanization on PM2. 5 concentrations: A case study from the Chengdu-Chongqing urban agglomeration in China. International Journal of Environmental Research and Public Health, 18(20), 10609.

31. Yao, Z., Hawi, P., Aitharaju, V., Mahishi, J., & Ghanem, R. (2023). Cross scale simulation of fiber-reinforced composites with uncertainty in machine learning. In Proceedings of the American Society for Composites-Thirty-Eighth Technical Conference.

32. Wang, J. (2023, July). A multi-task learning and transfer learning-based model for crop leaf disease identification. In 2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) (pp. 348-355). IEEE.

Downloads

Published

2026-05-10

Issue

Vol. 6 No. 01 (2026): Journal of geospace science

Section

Articles

License

Copyright (c) 2026 Akari Yamaguchi, Boyce Diane, Oceane Picard (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All articles published in the Journal of Geospace Science (JOGSS) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows others to share, copy, distribute, and adapt the work, provided proper attribution is given to the original author(s) and source.