Machine learning-based shear bearing capacity of concrete columns confined by transverse reinforcement subjected to lateral cyclic loading

• Autorzy: Hou Chongchi , Lv Yilei , Zheng Wenzhong , Zhang Yichao

Identyfikatory

DOI

10.1007/s43452-024-01080-8

• Języki publikacji: EN

Abstrakty

EN

The shear bearing capacity of confined concrete columns subjected to lateral cyclic loading is an important mechanical property in investigating seismic behavior of concrete buildings. However, it is still difficult to accurately predict shear bearing capacity of confined concrete columns using traditional analysis methods owing to its complex mechanical principle and indeterminate multivariable interrelationship. In this paper, an experimental study of 15 confined concrete columns subjected to lateral cyclic loading was conducted to explore the seismic behavior of confined concrete columns. Moreover, ANN and SVR models were established to accurately estimate the shear bearing capacity of confined concrete columns based on a reliable test database consisting of 121 specimens conducted in this study and published literatures. Nine key parameters were considered as input variables, including cross-sectional area of core concrete, unconfined concrete compressive strength, shear span ratio, axial compression ratio, volumetric ratio of transverse reinforcement, yield strength of transverse reinforcement, longitudinal reinforcement ratio, yield strength of longitudinal reinforcement, and confinement type. Additionally, the model sensitivity analysis was conducted to investigate the impact of parameters on shear bearing capacity of confined concrete columns. Finally, the ANN and SVR models were evaluated by comparing with five existing predicted methods and experimental results indicating that the ANN and SVM models have enough accuracy and reliability in predicting shear bearing capacity of confined concrete columns subjected to lateral cyclic loading.

Słowa kluczowe

EN

confined concrete column; shear bearing capacity; artificial neural networks; support vector regression; seismic performance

PL

kolumna betonowa; sztuczna sieć neuronowa; wytrzymałość sejsmiczna

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2025
• Tom: Vol. 25, no. 1
• Strony: art. no. e7, 2025
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Hou Chongchi

• School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China

autor

Lv Yilei

• School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China

autor

Zheng Wenzhong

• School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China

• https://orcid.org/0009-0003-4632-1264

autor

Zhang Yichao

• School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China

Bibliografia

• 1. Han B, Shin S, Bahn B. A model of confined concrete in high-strength reinforced concrete tied columns. Mag Concr Res.2003;55(3):203–14.
• 2. Hou CC, Zheng WZ. Review of studies on concrete columns confined by lateral reinforcement under axial compression and lateral cyclic loading. Struct Concr. 2022;24:1–28.
• 3. Paultre P, Legeron F, Mongcau D. Influence of concrete strength and transverse reinforcement yield strength on behavior of high-strength concrete columns. ACI Struct J. 2001;98(4):490–501.
• 4. Li YZ, Cao SY, Jing DH. Concrete columns reinforced with high-strength steel subjected to reversed cycle loading. ACI Struct J. 2018;115(4):1037–48.
• 5. Hou CC, Zheng WZ, Liu PF, Wang QH, Qi SB. Seismic performance of concrete columns confined by high-strength stirrups. Arch Civ Mech Eng. 2023;23:69.
• 6. ACI Committee 318. Building Code Requirements for Structural Concrete (ACI 318–19) and Commentary. American Concrete Institute, Farmington Hills, 2019.
• 7. NZS3101. Concrete structures standard Part 1—The design of concrete structures. Standard Association of New Zealand, Wel-lington, New Zealand, 2006.
• 8. CEB-FIB Bulletin 66. Mode code final draft-Volume 2. Laus-anne, Switzerland: Fédération Internationale du Béton, 20109. GB50010–2010. Code for design of concrete structures. China Architecture & Building Press, Beijing, China, 2011.
• 10. Priestley MJN, Verma R, Xiao Y. Seismic shear strength of rein-forced concrete column. J Struct Eng. 1994;120(8):2310–29.
• 11. Xiao Y, Martirossyan A. Seismic performance of high-strength concrete columns. J Struct Eng. 1998;124(3):241–51.
• 12. Martirossya A, Xiao Y. Flexural-shear behavior of high-strength concrete short columns. Earthq Spectra. 2001;17(4):679–95.
• 13. Sezen H, Moehle J. Shear strength model for lightly reinforced concrete columns. J Struct Eng. 2004;130(11):1692–703.
• 14. Sezen H. Shear deformation model for reinforced concrete columns. Struct Eng Mech. 2008;28(1):39–52.
• 15. Wang Z, Wang JQ, Zhu JZ, Zhang J. A simplified method to assess seismic behavior of reinforced concrete columns. Struct Concr.2020;21:151–68.
• 16. Jenkins WM. A neural network for structural reanalysis. Comput Struct. 1999;72:687–98.
• 17. Chang W, Zheng WZ. Estimation of compressive strength of stir-rup-confined circular columns using artificial neural networks. Struct Concr. 2019;20:1328–39.
• 18. Mehmet AK, Murat C, Musa HA, Alper I. Estimation of flexural capacity of quadrilateral FRP-confined RC columns using combined artificial neural network. Eng Struct. 2012;42:23–32.
• 19. Hosein N, Masoomeh M, Payam P. Failure mode prediction of reinforced concrete columns using machine learning methods. Eng Struct. 2021;248:113263.
• 20. Luo H, Paal S. Machine learning-based backbone curve model of reinforced concrete columns subjected to cyclic loading reversals. J Comput Civ Eng. 2018;32(5):04018042.
• 21. Paal S, Jeon J, Brilakis L, DesRoches R. Automated damage in dexestimation of reinforced concrete columns for post-earthquake evaluations. J Struct Eng. 2015;141(9):04014228.
• 22. Lattanzi D, Miller G, Eberhard M, Haraldsson O. Bridge column maximum drift estimation via computer vision. J Comput CivEng. 2016;30(4):04015051.
• 23. GB/T228.1–2010. Metallic materials-tensile testing-Part 1:method of test at room temperature. Standards Press of China, Beijing, China, 2011.
• 24. JGJ/T 101–2015. Specification for seismic test of buildings. China Building Industry Press, Beijing, China, 2015.
• 25. Hou CC, Zheng WZ, Li S, Wu XH. Experimental investigation of full-scale concrete columns confined by high-strength transverse reinforcement subjected to lateral cyclic loading. Arch Civ MechEng. 2020;20:115.
• 26. Yang KH. Flexural behavior of RC columns using wire ropes a slateral reinforcement. Mag Concr Res. 2012;64(3):269–81.
• 27. He SF, Deng AC. Seismic behavior of ultra-high performance concrete short columns confined with high-strength reinforcement. KSCE J Civ Eng. 2019;23(12):5183–93.
• 28. Ahn JM, Lee JY, Bahn BY, Shin SW. An experimental study of the behavior of high-strength reinforced concrete columns subjected to reversed cyclic shear under constant axial compression. MagConcr Res. 2000;52(3):209–18.
• 29. Yang K, Shi QX, Meng H, Men JJ. Axial compression ratio limits of HSC columns confined with high-strength stirrups. Adv MaterRes. 2011;163–167:1024–8.
• 30. Lam SSE, Wu B, Wang ZY, Wong YL, Chau KT. Behavior of rectangular columns with low lateral confinement ratio. StructEng Mech Comput. 2001;2:977–84.
• 31. Ozcebe G, Saatcioglu M. Confinement of concrete columns for seismic loading. ACI Struct J. 1987;84(4):308–15.
• 32. Hwang SK, Yun HD. Effects of transverse reinforcement on flexural behavior of high-strength concrete columns. Eng Struct.2004;26(1):1–12.
• 33. Ho JCM, Pam HJ. Inelastic design of low-axially loaded high-strength reinforced concrete column. Eng Struct.2003;25(8):1083–96.
• 34. Xiao JZ, Zhang C. Seismic behavior of RC columns with circular, square and diamond sections. Constr Build Mater.2008;22:801–10.
• 35. Mo YL, Wang SJ. Seismic behavior of RC columns with varioustie configurations. J Struct Eng. 2000;126(10):1122–30.
• 36. Watson S, Park R. Simulated seismic load tests on reinforced concrete columns. J Struct Eng. 1994;120(6):1825–49.
• 37. Su JS, Wang JJ, Bai ZZ, Wang WB, Zhao DX. Influence of reinforcement buckling on the seismic performance of reinforced concrete columns. Eng Struct. 2015;103:174–88.
• 38. Su JS, Wang JJ, Li ZX, Liang X. Effect of reinforcement grade and concrete strength on seismic performance of reinforced concrete bridge piers. Eng Struct. 2019;198:109512.
• 39. Li S, Zheng WZ, Xu T, Wang Y. Artificial neural network model for predicting the local compression capacity of stirrups-confined concrete. Structures. 2022;41:943–56.
• 40. Daliakopoulos IN, Coulibaly P, Tsanis IK. Ground water level forecasting using artificial neural networks. J Hydrol.2015;309:229–40.
• 41. Panahi H, Genikomsou AS. A machine-learning-based model for seismic performance assessment of interior slab-column connections. Soil Dyn Earthq Eng. 2023;171:107943.
• 42. Ahmad MS, Adnan SM, Zaidi S, Bhargava P. A novel support vector regression (SVR) model for the prediction of splicestrength of the unconfined beam specimens. Constr Build Mater.2020;248:118475.
• 43. Wang Z, Liu TX, Long ZL, Wang JQ, Zhang J. A machine-learning–based model for prediction the effective stiffness of precast concrete columns. Eng Struct. 2022;260:114224.
• 44. Award M, Khanna R. Support vector regression. Effic LearnMach. 2015. https://doi.org/10.1007/978-1-4302-5990-9_4.
• 45. Liu T, Wang Z, Zeng H, Wang J. Machine-learning-based models to predict shear transfer strength of concrete joints. Eng Struct.2021;249:113253.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025)