دوره 18، شماره 36 - ( 9-1401 )
جلد 18 شماره 36 صفحات 31-24 |
برگشت به فهرست نسخه ها
1- دانشگاه دریانوردی و علوم دریایی چابهار
2- دانشگاه صنعتی امیر کبیر
2- دانشگاه صنعتی امیر کبیر
چکیده: (1149 مشاهده)
ارزیابی استحکام نهایی ورق‌های تقویت شده تشکیل‌دهنده سازه کشتی، اولین مرحله در ارزیابی استحکام نهایی آن است. باگذشت زمان و افزایش عمر سازه، خرابی‌هایی نظیر ایجاد ترک سبب کاهش ظرفیت باربری سازه کشتی می‌شوند. هدف اصلی این مقاله ارائه روشی مبتنی بر یادگیری ماشین با استفاده از الگوریتم برای محاسبه استحکام نهایی فشاری ورق‌های تقویت‌شده با خرابی ترک با استفاده از نتایج تحلیل‌های متعدد المان محدود است. برای دستیابی به بهترین نتایج ممکن از الگوریتم XGBoost، بخشی هایپرپارامترهای موجود در این الگوریتم با استفاده از روش بهینه‌سازی بیزین، بهینه شده است. نتایج حاصل از این روش نشان می‌دهد که دقت استفاده از الگوریتم بهینه شده XGBoostبسیار بالاتر از روش‌های متداول بر مبنای رگرسیون خطی است.
فهرست منابع
1. J. K. Paik and A. K. Thayamballi, "Ultimate strength of ageing ships," Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, vol. 216, no. 1, pp. 57-77, Jun. 2002, doi: 10.1243/147509002320382149. [DOI:10.1243/147509002320382149]
2. J. K. Paik and Y. V. Satish Kumar, "Ultimate Strength of Stiffened Panels With Cracking Damage Under Axial Compression or Tension," Journal of Ship Research, vol. 50, no. 03, pp. 231-238, Sep. 2006, doi: 10.5957/jsr.2006.50.3.231. [DOI:10.5957/jsr.2006.50.3.231]
3. Y. Hu, W. Cui, and P. Terndrup Pedersen, "Maintained ship hull xcgirxcder ultimate strength reliability considering corrosion and fatigue," Marine Structures, vol. 17, no. 2, pp. 91-123, Mar. 2004, doi: 10.1016/j.marstruc.2004.06.001. [DOI:10.1016/j.marstruc.2004.06.001]
4. J. Bai, "Time-variant ultimate strength reliability assessment of ship hulls considering corrosion and fatigue," PhD thesis, University of California, Berkeley, 2006.
5. M. R. Zareei and M. Iranmanesh, "Ultimate strength formulation of stiffened panels under in-plane compression or tension with cracking damage," J. nav. arch. mar. engg., vol. 15, no. 1, pp. 1-16, Jun. 2018, doi: 10.3329/jname.v15i1.31668. [DOI:10.3329/jname.v15i1.31668]
6. M. R. Zareei, M. R. Khedmati, and P. Rigo, "Application of artificial neural networks to the evaluation of the ultimate strength of uniaxially compressed welded stiffened aluminium plates," Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, vol. 226, no. 3, pp. 197-213, Aug. 2012, doi: 10.1177/1475090212445865. [DOI:10.1177/1475090212445865]
7. Z. ul R. Tahir and P. Mandal, "Artificial neural network prediction of buckling load of thin cylindrical shells under axial compression," Engineering Structures, vol. 152, pp. 843-855, Dec. 2017, doi: 10.1016/j.engstruct.2017.09.016. [DOI:10.1016/j.engstruct.2017.09.016]
8. Z. ul R. Tahir, P. Mandal, M. T. Adil, and F. Naz, "Application of artificial neural network to predict buckling load of thin cylindrical shells under axial compression," Engineering Structures, vol. 248, p. 113221, Dec. 2021, doi: 10.1016/j.engstruct.2021.113221. [DOI:10.1016/j.engstruct.2021.113221]
9. Z. Sun et al., "Prediction of compression buckling load and buckling mode of hat-stiffened panels using artificial neural network," Engineering Structures, vol. 242, p. 112275, Sep. 2021, doi: 10.1016/j.engstruct.2021.112275. [DOI:10.1016/j.engstruct.2021.112275]
10. A. Kaveh, A. Dadras Eslamlou, S. M. Javadi, and N. Geran Malek, "Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders," Acta Mech, vol. 232, no. 3, pp. 921-931, Mar. 2021, doi: 10.1007/s00707-020-02878-2. [DOI:10.1007/s00707-020-02878-2]
11. M. Steurer, R. J. Hill, and N. Pfeifer, "Metrics for evaluating the performance of machine learning based automated valuation models," Journal of Property Research, vol. 38, no. 2, pp. 99-129, Apr. 2021, doi: 10.1080/09599916.2020.1858937. [DOI:10.1080/09599916.2020.1858937]
12. T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA, Aug. 2016, pp. 785-794. doi: 10.1145/2939672.2939785. [DOI:10.1145/2939672.2939785]
13. V.-H. Truong, G. Papazafeiropoulos, Q.-V. Vu, V.-T. Pham, and Z. Kong, "Predicting the patch load resistance of stiffened plate girders using machine learning algorithms," Ocean Engineering, vol. 240, p. 109886, Nov. 2021, doi: 10.1016/j.oceaneng.2021.109886. [DOI:10.1016/j.oceaneng.2021.109886]
14. "xgboost.readthedocs.io."
15. J. Guo et al., "An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring," Computer Networks, vol. 151, pp. 166-180, Mar. 2019, doi: 10.1016/j.comnet.2019.01.026. [DOI:10.1016/j.comnet.2019.01.026]
16. W. Zhang, C. Wu, H. Zhong, Y. Li, and L. Wang, "Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization," Geoscience Frontiers, vol. 12, no. 1, pp. 469-477, Jan. 2021, doi: 10.1016/j.gsf.2020.03.007. [DOI:10.1016/j.gsf.2020.03.007]
17. Y. Xia, C. Liu, Y. Li, and N. Liu, "A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring," Expert Systems with Applications, vol. 78, pp. 225-241, Jul. 2017, doi: 10.1016/j.eswa.2017.02.017. [DOI:10.1016/j.eswa.2017.02.017]
18. "https://github.com/fmfn/BayesianOptimization."
19. J. Zhou, Y. Qiu, S. Zhu, D. J. Armaghani, M. Khandelwal, and E. T. Mohamad, "Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization," Underground Space, vol. 6, no. 5, pp. 506-515, Oct. 2021, doi: 10.1016/j.undsp.2020.05.008. [DOI:10.1016/j.undsp.2020.05.008]
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