Direct Shear Strength Prediction for Precast Concrete Joints Using the Machine Learning Method
Publication: Journal of Bridge Engineering
Volume 27, Issue 5
Abstract
Precast segmental concrete beams (PSCBs) are being increasingly applied in bridges worldwide benefitting from the advantages of accelerated bridge construction. It is of importance to accurately predict the direct shear strength (DSS) of precast concrete joints (PCJs) for ensuring the safe structural design of PSCBs. However, existing prediction models of PCJs’ DSS are deemed inaccurate and unreliable when numerous parameters are varied in wide ranges. This study aims to establish an accurate and reliable prediction model for PCJs’ DSS using a machine learning algorithm called support vector regression (SVR). A PCJs’ DSS database of 304 test results with 23 input parameters was assembled from the literature. A model training procedure was conducted through stratified train-test split, feature scaling, feature selection, and two-step grid-search hyperparameter tuning. A new correlation matrix–based feature selection method was proposed, and three SVR models with different feature combinations were trained for validating the selection method. The trained SVR models were experimentally validated and compared with six existing mechanical models through two groups of performance indicators. A reasonable interpretation for the SVR model with the selected features in the proposed selection method was done using the combination of partial dependence (PD) and individual conditional expectation (ICE) plots. The results show that the SVR algorithm can be deemed feasible to accurately and reliably predict the DSS of PCJs. The proposed feature selection method is beneficial to the prediction performance of the SVR model. It is impossible for the typical mechanical models to achieve a similar prediction performance of the SVR model. The influence of each input parameter on the DSS of PCJs is recognized and depicted, which can offer useful information for further developing new mechanical models for predicting the DSS of PCJs with higher prediction performance in future research.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The work described in this paper was financially supported by the National Natural Science Foundation of China (Grant No. U1934205). The authors also gratefully acknowledge the financial support from the China Scholarship Council (CSC).
References
AASHTO. 2003. Guide specifications for design and construction of segmental concrete bridges: 2003 Interim revisions. 2nd ed. Washington, DC: AASHTO.
Ahmed, G. H., and O. Q. Aziz. 2019a. “Shear strength of joints in precast posttensioned segmental bridges during 1959–2019, review and analysis.” Structures 20: 527–542. https://doi.org/10.1016/j.istruc.2019.06.007.
Ahmed, G. H., and O. Q. Aziz. 2019b. “Shear behavior of dry and epoxied joints in precast concrete segmental box girder bridges under direct shear loading.” Eng. Struct. 182 (1): 89–100. https://doi.org/10.1016/j.engstruct.2018.12.070.
Ahmad, M. S., S. M. Adnan, S. Zaidi, and P. Bhargava. 2020. “A novel support vector regression (SVR) model for the prediction of splice strength of the unconfined beam specimens.” Constr. Build. Mater. 248: 118475. https://doi.org/10.1016/j.conbuildmat.2020.118475.
Bakhoum, M. M. 1990. “Shear behaviour and design of joints in precast concrete segmental bridges.” Ph.D. thesis, Dept. of Civil Engineering, Massachusetts Institute of Technology.
Beattie, S. M. 1989. “Behavioral improvements in segmental concrete bridge joints through the use of steel fibers.” M.S. thesis, Dept. of Civil Engineering, Massachusetts Institute of Technology.
Buyukozturk, O., M. M. Bakhoum, and S. M. Beattie. 1990. “Shear behavior of joints in precast concrete segmental bridges.” J. Struct. Eng. 116 (12): 3380–3401. https://doi.org/10.1061/(ASCE)0733-9445(1990)116:12(3380).
Chou, J. S., N. T. Ngo, and A. D. Pham. 2016. “Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression.” J. Comput. Civ. Eng. 30 (1): 04015002. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000466.
Cortes, C., and V. Vapnik. 1995. “Support vector machine.” Mach. Learn. 20 (3): 273–297.
Davaadorj, O., P. M. Calvi, and J. F. Stanton. 2020. “Shear stress transfer across concrete-to-concrete interfaces: Experimental evidence and available strength models.” PCI J. 65 (4): 87–111. https://doi.org/10.15554/pcij65.4-04.
Dibike, Y. B., S. Velickov, D. Solomatine, and M. B. Abbott. 2001. “Model induction with support vector machines: Introduction and applications.” J. Comput. Civ. Eng. 15 (3): 208–216. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:3(208).
Dormann, C. F., et al. 2013. “Collinearity: A review of methods to deal with it and a simulation study evaluating their performance.” Ecography 36 (1): 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x.
Feng, D. C., Z. T. Liu, X. D. Wang, Z. M. Jiang, and S. X. Liang. 2020. “Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm.” Adv. Eng. Inf. 45: 101126. https://doi.org/10.1016/j.aei.2020.101126.
Feng, D. C., W. J. Wang, S. Mangalathu, G. Hu, and T. Wu. 2021a. “Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements.” Eng. Struct. 235: 111979. https://doi.org/10.1016/j.engstruct.2021.111979.
Feng, D. C., B. Cetiner, M. R. Azadi Kakavand, and E. Taciroglu. 2021b. “Data-driven approach to predict the plastic hinge length of reinforced concrete columns and its application.” J. Struct. Eng. 147 (2): 04020332. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002852.
Fu, B., and D. C. Feng. 2021. “A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams.” J. Build. Eng. 36: 102118. https://doi.org/10.1016/j.jobe.2020.102118.
Gao, X., and C. Lin. 2021. “Prediction model of the failure mode of beam–column joints using machine learning methods.” Eng. Fail. Anal. 120: 105072. https://doi.org/10.1016/j.engfailanal.2020.105072.
Gopal, B. A., F. Hejazi, M. Hafezolghorani, and Y. L. Voo. 2020. “Shear strength of dry and epoxy joints for ultra-high-performance fiber reinforced concrete.” ACI Struct. J. 117 (1): 279–288. https://doi.org/10.14359/51718078.
Gui, G., H. Pan, Z. Lin, Y. Li, and Z. Yuan. 2017. “Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection.” KSCE J. Civ. Eng. 21 (2): 523–534. https://doi.org/10.1007/s12205-017-1518-5.
Hariri-Ardebili, M., and F. Pourkamali-Anaraki. 2018. “Support vector machine based reliability analysis of concrete dams.” Soil Dyn. Earthquake Eng. 104: 276–295. https://doi.org/10.1016/j.soildyn.2017.09.016.
Hasni, H., A. H. Alavi, P. Jiao, and N. Lajnef. 2017. “Detection of fatigue cracking in steel bridge girders: A support vector machine approach.” Arch. Civ. Mech. Eng. 17 (3): 609–622. https://doi.org/10.1016/j.acme.2016.11.005.
Hoang, N. D., X. L. Tran, and H. Nguyen. 2020. “Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model.” Neural Comput. Appl. 32 (11): 7289–7309. https://doi.org/10.1007/s00521-019-04258-x.
Hsu, C. W., C. C. Chang, and C. J. Lin. 2003. A practical guide to support vector classification. Technical report, Dept. of Computer Science, National Taiwan University, Taipei.
Huang, J., Y. Sun, and J. Zhang. 2021. “Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm.” Eng. Comput. 1–18. https://doi.org/10.1007/s00366-021-01305-x.
Issa, M., and H. Abdalla. 2007. “Structural behavior of single key joints in precast concrete segmental bridges.” J. Bridge Eng. 12 (3): 315–324. https://doi.org/10.1061/(ASCE)1084-0702(2007)12:3(315).
Jiang, H., L. Chen, Z. J. Ma, and W. Feng. 2015. “Shear behavior of dry joints with castellated keys in precast concrete segmental bridges.” J. Bridge Eng. 20 (2): 04014062. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000649.
Jiang, H., R. Wei, Z. John Ma, Y. Li, and Y. Jing. 2016. “Shear strength of steel fiber-reinforced concrete dry joints in precast segmental bridges.” J. Bridge Eng. 21 (11): 04016085. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000968.
Jiang, H., J. H. Feng, A. R. Liu, and T. L. Liang. 2019. “Effect of specimen thickness and coarse aggregate size on shear strength of single-keyed dry joints in precast concrete segmental bridges.” Struct. Concr. 20 (3): 955–970. https://doi.org/10.1002/suco.201800054.
Kaneko, Y., J. J. Connor, T. C. Triantafillou, and C. K. Leung. 1993. “Fracture mechanics approach for failure of concrete shear key. I: Theory.” J. Eng. Mech. 119 (4): 681–700. https://doi.org/10.1061/(ASCE)0733-9399(1993)119:4(681).
Kang, M. C., D. Y. Yoo, and R. Gupta. 2021. “Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete.” Constr. Build. Mater. 266: 121117. https://doi.org/10.1016/j.conbuildmat.2020.121117.
Keshtegar, B., A. Gholampour, D. K. Thai, O. Taylan, and N. T. Trung. 2021a. “Hybrid regression and machine learning model for predicting ultimate condition of FRP-confined concrete.” Compos. Struct. 262: 113644. https://doi.org/10.1016/j.compstruct.2021.113644.
Keshtegar, B., M. L. Nehdi, R. Kolahchi, N. T. Trung, and M. Bagheri. 2021b. “Novel hybrid machine learning model for predicting shear strength of reinforced concrete shear walls.” Eng. Comput. 1–12. https://doi.org/10.1007/s00366-020-01081-0.
Kim, Y. J., W. J. Chin, and S. J. Jeon. 2018. “Interface shear strength at joints of ultra-high-performance concrete structures.” Int. J. Concr. Struct. Mater. 12 (1): 1–14. https://doi.org/10.1186/s40069-018-0237-8.
Koseki, K., and J. E. Breen. 1983. Exploratory study of shear strength of joints for precast segmental bridges. Research Rep. No. 248-1. Austin, TX: Texas State Dept. of Highways and Public Transportation, Center for Transportation Research, Bureau of Engineering Research, Univ. of Texas at Austin.
Liu, T. 2017. “Experimental and theoretical research on shear behavior of joints in precast UHPC segmental bridges.” [In Chinese.] M.S. thesis, School of Civil Engineering, Southeast Univ.
Liu, T., Z. Wang, J. Guo, and J. Wang. 2019. “Shear strength of dry joints in precast UHPC segmental bridges: Experimental and theoretical research.” J. Bridge Eng. 24 (1): 04018100. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001323.
Luo, H., and S. G. Paal. 2018. “Machine learning-based backbone curve model of reinforced concrete columns subjected to cyclic loading reversals.” J. Comput. Civ. Eng. 32 (5): 04018042. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000787.
Mangalathu, S., H. Jang, S. H. Hwang, and J. S. Jeon. 2020. “Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls.” Eng. Struct. 208: 110331. https://doi.org/10.1016/j.engstruct.2020.110331.
Olalusi, O. B., and P. O. Awoyera. 2021. “Shear capacity prediction of slender reinforced concrete structures with steel fibers using machine learning.” Eng. Struct. 227: 111470. https://doi.org/10.1016/j.engstruct.2020.111470.
Olalusi, O. B., and P. Spyridis. 2020. “Machine learning-based models for the concrete breakout capacity prediction of single anchors in shear.” Adv. Eng. Software 147: 102832. https://doi.org/10.1016/j.advengsoft.2020.102832.
Pal, M., and S. Deswal. 2011. “Support vector regression based shear strength modelling of deep beams.” Comput. Struct. 89 (13–14): 1430–1439. https://doi.org/10.1016/j.compstruc.2011.03.005.
Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, and E. Duchesnay. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn Res. 12: 2825–2830.
Ranković, V., N. Grujović, D. Divac, and N. Milivojević. 2014. “Development of support vector regression identification model for prediction of dam structural behaviour.” Struct. Saf. 48: 33–39. https://doi.org/10.1016/j.strusafe.2014.02.004.
Reuter, U., A. Sultan, and D. S. Reischl. 2018. “A comparative study of machine learning approaches for modeling concrete failure surfaces.” Adv. Eng. Software 116: 67–79. https://doi.org/10.1016/j.advengsoft.2017.11.006.
Rombach, G. A. 2002. “Precast segmental box girder bridges with external prestressing: Design and construction.” Segmental Bridges 19 (2): 1–15.
Rombach, G. A. 2004. “Dry joint behavior of hollow box girder segmental bridges.” In Proc., Int. Fib Symp.—Segmental Construction in Concrete. Lausanne, Switzerland: International Federation for Structural Concrete (fib).
Shariati, M., M. S. Mafipour, B. Ghahremani, F. Azarhomayun, M. Ahmadi, N. T. Trung, and A. Shariati. 2020. “A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement.” Eng. Comput. 38: 757–779.
Smola, A. J., and B. Schölkopf. 2004. “A tutorial on support vector regression.” Stat. Comput. 14 (3): 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88.
Su, M., Q. Zhong, H. Peng, and S. Li. 2021. “Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete.” Constr. Build. Mater. 270: 121456. https://doi.org/10.1016/j.conbuildmat.2020.121456.
Taylor, K. E. 2001. “Summarizing multiple aspects of model performance in a single diagram.” J. Geophys. Res. Atmos. 106 (D7): 7183–7192. https://doi.org/10.1029/2000JD900719.
Thakur, M. S., S. M. Pandhiani, V. Kashyap, A. Upadhya, and P. Sihag. 2021. “Predicting bond strength of FRP bars in concrete using soft computing techniques.” Arab. J. Sci. Eng. 46 (5): 4951–4969. https://doi.org/10.1007/s13369-020-05314-8.
Turmo, J., G. Ramos, and A. C. Aparicio. 2006. “Shear strength of match cast dry joints of precast concrete segmental bridges: Proposal for Eurocode 2.” Mater. Constr. 56 (282): 45–52. https://doi.org/10.3989/mc.2006.v56.i282.26.
UNESCO. 1971. Reinforced concrete, An international manual. London: Butterworths.
Vapnik, V. N. 1999. “An overview of statistical learning theory.” IEEE Trans. Neural Networks 10 (5): 988–999. https://doi.org/10.1109/72.788640.
Voo, Y., S. Foster, and C. C. Voo. 2015. “Ultrahigh-performance concrete segmental bridge technology: Toward sustainable bridge construction.” J. Bridge Eng. 20 (8): B5014001.
Wang, J., T. Liu, and Z. Wang. 2017. “Calculation method for shear strength of concrete keyed joints considering bending effects.” [In Chinese.] J. Southeast Univ. 47 (3): 553–558.
Yan, K., and C. Shi. 2010. “Prediction of elastic modulus of normal and high strength concrete by support vector machine.” Constr. Build. Mater. 24 (8): 1479–1485. https://doi.org/10.1016/j.conbuildmat.2010.01.006.
Yang, I. H., and K. C. Kim. 2012. “Strength of joint in floating structures constructed with precast concrete modules.” J. Navig. Port Res. 36 (3): 197–204. https://doi.org/10.5394/KINPR.2012.36.3.197.
Yuan, A., C. Yang, J. Wang, L. Chen, and R. Lu. 2019. “Shear behavior of epoxy resin joints in precast concrete segmental bridges.” J. Bridge. Eng. 24 (4): 04019009. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001362.
Yuan, A., X. Zhao, and R. Lu. 2020. “Experimental investigation on shear performance of fiber-reinforced high-strength concrete dry joints.” Eng. Struct. 223: 111159. https://doi.org/10.1016/j.engstruct.2020.111159.
Yuvaraj, P., A. R. Murthy, N. R. Iyer, S. K. Sekar, and P. Samuid. 2013. “Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete beams.” Eng. Fract. Mech. 98 (1): 29–43. https://doi.org/10.1016/j.engfracmech.2012.11.014.
Zegler, C., and H. Rusch. 1961. “Der einfuss von fugen auf die feistigkeit von fertigteilen.” [In German.] Beton-und Stahlebetonbau 56 (10): 234–237.
Zhi, Q., X. Xiong, W. Yang, S. Liu, and J. Xiong. 2020. “Experimental study on the shear behavior of precast wall concrete joints with/without dowel reinforcement.” Materials 13 (7): 1726. https://doi.org/10.3390/ma13071726.
Zhou, X., N. Mickleborough, and Z. Li. 2005. “Shear strength of joints in precast concrete segmental bridges.” ACI Struct. J. 102 (1): 3–11.
Zhou, Y., W. Su, L. Ding, H. Luo, and P. E. Love. 2017. “Predicting safety risks in deep foundation pits in subway infrastructure projects: A support vector machine approach.” J. Comput. Civ. Eng. 31 (5): 04017052. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000700.
Zhou, Z. 2016. Machine learning. [In Chinese.] Beijing: Tsinghua University Press.
Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 27 • Issue 5 • May 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Sep 10, 2021
Accepted: Jan 18, 2022
Published online: Mar 11, 2022
Published in print: May 1, 2022
Discussion open until: Aug 11, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Zhen Wang, Tongxu Liu, Zilin Long, Jingquan Wang, Jian Zhang, Predicting the drift capacity of precast concrete columns using explainable machine learning approach, Engineering Structures, 10.1016/j.engstruct.2023.115771, 282, (115771), (2023).
- Congzhen Xiao, Baojuan Qiao, Jianhui Li, Zhiyong Yang, Jiannan Ding, Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques, Advances in Civil Engineering, 10.1155/2022/2923069, 2022, (1-15), (2022).
- Lei Pan, Yuanfeng Wang, Kai Li, Xiaohui Guo, Predicting compressive strength of green concrete using hybrid artificial neural network with genetic algorithm, Structural Concrete, 10.1002/suco.202200034, (2022).