Korozyona Uğramış Betonarme Kirişler için Kesme Dayanımını Belirlemeye Yönelik Basitleştirilmiş Bir Yaklaşım

Abstract

Yapı ömrü boyunca yapım ya da kullanım kusuru sayılabilecek korozyon hasarı yapı elemanları için önemli bir parametredir. Korozyon sebebiyle betonarme elemanlarda dayanım kaybı görülmekte bu da yapı performansını etkileyen önemli bir parametre olmaktadır. Donatısı korozyona uğramış kirişlerin kayma mukavemetinin belirlenmesi, yapı elamanında dayanım kaybı, tasarım ve güçlendirme kriterleri açısından önemli olmaktadır. Bu çalışmada yapay zekâ algoritmaları ile betonarme kiriş deneylerinden elde edilen kesme dayanımı değerlerinin deneysel çalışmaya gerek kalmadan belirlenmesi amaçlanmaktadır. Bu kapsamda literatürde gerçekleştirilmiş korozyona uğramış betonarme kiriş deneyleri verileri toparlanmış, deney parametrelerine bağlı olarak kirişlerin nihai kesme dayanımı değerleri tespit edilmiştir. Dayanım tahmini makine öğrenmesi regresyon algoritmalarından XGBoost ve AdaBoost ile gerçekleştirilmiştir. Elde edilen sonuçlar R2, RMSE ve MAE performans metrikleri ile değerlendirilmiş ve yüksek tahmin başarısına ulaşılmıştır. Çalışma göstermektedir ki deneysel verilere bağlı öğrenme gerçekleştirebilen bu sistemler ile üretim parametreleri bilinen ve korozyona uğramış kesme dayanımı değerlerini deneysel ölçümlere ihtiyaç duymadan tahmin etmek mümkündür.

Keywords

• Korozyonlu kiriş
• kesme dayanımı
• makine öğrenmesi
• XGBoost
• AdaBoost

A Simplified Approach to Determine Shear Strength for Corroded RC Beams

Abstract

Corrosion damage, which can be considered as a construction or usage defect during the life of the structure, is an important parameter for the structural elements. Strength loss is observed in reinforced concrete (RC) elements due to corrosion, which is an important parameter affecting the performance of the building. Determining the shear strength of beams with corroded reinforcement is important in terms of strength loss, design, and reinforcement criteria in the structural element. In this context, the data of the corroded RC beam experimental tests carried out in the literature were collected and the ultimate shear strength values of the beams were determined depending on the test parameters. Strength estimation was performed with machine learning regression algorithms XGBoost and AdaBoost. The results obtained were evaluated with R2, RMSE and MAE performance metrics and high estimation success was achieved. The study shows that with these systems, which can perform learning based on experimental data, it is possible to estimate the shear strength values of corroded beams with known production parameters without the need for experimental measurements.

Keywords

• Corroded beam; shear strength
• machine learning; XGBoost
• AdaBoost.

References

1. [1] D. Li, R. Wei, F. Xing, L. Sui, Y. Zhou, W. Wang, “Influence of non-uniform corrosion of steel bars on the seismic behavior of reinforced concrete columns”, Construction and Building Materials, 167, 20–32, 2018.
2. [2] S. Y. Yang, X. B. Song, H. X. Jia, X. Chen, X. L. Liu, “Experimental research on hysteretic behaviors of corroded reinforced concrete columns with different maximum amounts of corrosion of rebar”, Construction and Building Materials, 121, 319-32, 2016.
3. [3] X.H. Wang, F.Y. Liang, “Performance of RC columns with partial length corrosion”, Nuclear Engineering and Design, Volume 238, Issue 12, Pages 3194-3202, 2008.
4. [4] K.A. Soudki, T.G. Sherwood, “Behaviour of reinforced concrete beams strengthened with carbon fibre reinforced polymer laminates subjected to corrosion damage”, Canadian Journal of Civil Engineering, 2000, 27(5): 1005-1010, 2000.
5. [5] H.A. Razak, F.C. Choi, “The effect of corrosion on the natural frequency and modal damping of reinforced concrete beams”, Engineering Structures, 23 (2001) 1126–1133, 2001.
6. [6] T. El Maaddawy, K. Soudki, T. Topper, “Analytical model to predict nonlinear flexural behaviour of corroded reinforced concrete beams”, ACI Structural Journal, 102(4), 550–9, 2005.
7. [7] Y.G. Du, L.A. Clark, A.H.C. Chan, “Impact of reinforcement corrosion on ductile behavior of reinforced concrete beams” ACI Structural Journal, 104(3), 285–93, 2007.
8. [8] J. Rodriguez, L.M. Ortega, J. Casal, “Load carrying capacity of concrete structures with corroded reinforcement”, Construction and Building Materials, Volume 11, Issue 4, Pages 239-248, 1997.

9. [9] C. Higgins, W.C. Farrow, “Tests of Reinforced Concrete Beams with Corrosion Damaged Stirrups”, ACI Structural Journal, Vol. 103, Iss. 1, pp: 133-141, 2006.
10. [10] Z. Ye, W. Zhang, X. Gu, “Deterioration of shear behavior of corroded reinforced concrete beams, Engineering Structures”, Volume 168, Pages 708-720, 2018.
11. [11] O. Poupard, V. L’Hostis, S. Catinaud, I. Petre-Lazar, “Corrosion damage diagnosis of a reinforced concrete beam after 40 years natural exposure in marine environment”, Cement and Concrete Research 36 (2006) 504 – 520, 2006.
12. [12] K. Worden, G. Manson, “The Application of Machine Learning to Structural Health Monitoring. Philosophical Transactions of The Royal Society A: Mathematical”, Physical and Engineering Sciences, 2007, 365(1851), 515-537.
13. [13] G. Gui, H. Pan, Z. Lin, Y. Li, Z. Yuan, “Data-Driven Support Vector Machine with Optimization Techniques for Structural Health Monitoring and Damage Detection”, Ksce Journal of Civil Engineering, 21(2), 523-534, 2017.
14. [14] Y. Zhang, H. V. Burton, H. Sun, M. Shokrabadi, A” Machine Learning Framework for Assessing Post-Earthquake Structural Safety”, Structural Safety, (2018), 72, 1-16.
15. [15] S. Mangalathu, S. H. Hwang, E. Choi, J. S. Jeon, “Rapid Seismic Damage Evaluation of Bridge Portfolios Using Machine Learning Techniques”, Engineering Structures, (2019), 201, 109785.
16. [16] J.S. Jeon, A. Shafieezadeh, R. DesRoches, “Statistical models for shear strength of RC beam column joints using machine-learning techniques”, Earthq Eng Struct Dyn 2014;43:2075–95.
17. [17] A. Santos, E. Figueiredo, M. F. M. Silva, C. S. Sales, J. C. W. A. Costa, “Machine Learning Algorithms for Damage Detection: Kernel-Based Approaches”, Journal of Sound and Vibration, (2016), 363, 584-599.
18. [18] M. H. Rafiei, H., Adeli, “A Novel Machine Learning‐Based Algorithm to Detect Damage in High‐Rise Building Structures”, The Structural Design of Tall and Special Buildings, 26(18), (2017), E1400.
19. [19] A. C. Neves, I. González, J. Leander, R. Karoumi, “A New Approach to Damage Detection in Bridges Using Machine Learning”, In International Conference on Experimental Vibration Analysis for Civil Engineering Structures (Pp. 73-84), 2017, Springer, Cham.
20. [20] S. Mangalathu, S. H. Hwang, E. Choi, J. S. Jeon, “Rapid Seismic Damage Evaluation of Bridge Portfolios Using Machine Learning Techniques”, Engineering Structures, (2019), 201, 109785.
21. [21] Y. Okazaki, S. Okazaki, S. Asamoto, P. J. Chun, “Applicability of Machine Learning to A Crack Model in Concrete Bridges”, Computer‐Aided Civil and Infrastructure Engineering, (2020), 35(8), 775-792.
22. [22] Y. Cao, K. Wakil, R. Alyousef, K. Jermsittiparsert, L. Si Ho, H. Alabduljabbar, et al. “Application of extreme learning machine in behavior of beam to column connections”, Struct 2020;25:861–7.
23. [23] G. Dogan, M.H. Arslan, O.K. Baykan, “Determination of damage levels of RC columns with a smart system-oriented method”, Bulletin of Earthquake Engineering, 2020, 18(7): p. 3223-3245.
24. [24] X. Gao, C. Lin, “Prediction model of the failure mode of beam-column joints using machine learning methods”, Eng Fail Anal 2021;120.
25. [25] A. G ́eron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow:Concepts, tools, and techniques to build intelligent systems: O’Reilly Media; 2019. [26] T. Chen, C. Guestrin, “XGBoost: A scalable tree boosting system”, In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016. p. 785–94.
26. [27] H. Mo, H. Sun, J. Liu, S. Wei, “Developing window behavior models for residential buildings using XGBoost algorithm”, Energy and Buildings,Volume 205, 109564, 2019.
27. [28] B. Fu, D.C. Feng, “A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams”, J Build Eng 2021;36.
28. [29] Z.H. Lu, H. Li, W. Li, Y.G. Zhao, W. Dong, “An empirical model for the shear strength of corroded reinforced concrete beam”, Construct. Build. Mater. 188, 1234–1248, 2018.
29. [30] Y. Zhao, L. Petherbridge, L.P. Smith, et al., “Self-excision of the BAC sequences from the recombinant Marek's disease virus genome increases replication and pathogenicity”, Virol J 5, 19, 2008, https://doi.org/10.1186/1743-422X-5-19
30. [31] S. Xu, D. Niu, “Shear behavior of corroded simply supported reinforced concrete beam”, Jianzhu Jiegou Xuebao/Journal of Building Structures, Volume 25, Issue 5, Pages 98, 2004.
31. [32] X. Li, Y. Huiguang, "Degradation mechanism and predicting models of shearing capacity for corroded reinforced concrete beams." Journal of Xuzhou Institute of Technology, 25.4 (2010): 58-63, 2010.
32. [33] X. Jin, W.L., Jin, L.Y. Li, “Shear performance of reinforced concrete beams with corroded stirrups in chloride environment”, Corrosion Science, Volume 53, Issue 5, Pages 1794-1805, 2011,
33. [34] C.A. Juarez, B. Guevara, G. Fajardo, P., Castro-Borges, Ultimate and nominal shear strength in reinforced concrete beams deteriorated by corrosion, Engineering Structures, Volume 33, Issue 12, Pages 3189-3196, 2011.
34. [35] X. Xue, H. Seki, Z. W. Chen, "Shear capacity of RC beams containing corroded longitudinal bars." Proceedings of the Thirteenth East Asia-Pacific Conference on Structural Engineering and Construction (EASEC-13), 2013.
35. [36] S. Liu, The research on shear capacity of corroded rc beams, PhD Thesis, Master’s thesis, Central South University, China, 2013.
36. [37] A. Imam, A.K. Azad, “Prediction of residual shear strength of corroded reinforced concrete beams”, Int J Adv Struct Eng 8, 307–318, (2016), https://doi.org/10.1007/s40091-016-0133-x
37. [38] A. El-Sayed, R.R. Hussain, A. Shuraim, “Influence of stirrup corrosion on shear strength of reinforced concrete slender beams”, ACI Structural Journal, Volume 113, Issue 6, Pages 1223 – 1232, 2016.
38. [39] G. Van Rossum, F.L. Drake Jr, Python tutorial. Vol. 620., Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands, 1995.
39. [40] A. Abushanab, T.G.Wakjira, W. Alnahha, 2023, Machine Learning-Based Flexural Capacity Prediction of Corroded RC Beams with an Efficient and User-Friendly Tool, Sustainability , 15(6), 4824; https://doi.org/10.3390/su15064824.