Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction

Year 2024, , 279 - 289, 18.07.2024

Aybike Özyüksel Çiftçioğlu

https://doi.org/10.56554/jtom.1401261

Abstract

This research presents a comprehensive investigation into the accurate estimation of shear strength in rectangular reinforced concrete columns through advanced machine learning (ML) models. The study addresses the intricate challenge posed by shear strength complexity, which is crucial for evaluating column stability and ensuring structural integrity. Building upon a substantial dataset comprising 545 experimental observations sourced from diverse literature, this research establishes a robust foundation for predictive modeling. Four distinct ML regression models, Random Forest, Decision Tree, XGBoost, and LightGBM, are meticulously evaluated for their performance. The evaluation employs established metrics, including R2, RMSE, MAE, and MAPE to quantify their predictive capabilities. The outcomes highlight the models' robustness in capturing nuanced variations in shear strength, with impressive R2 values ranging from 93.6% to 93.9%, showcasing their exceptional ability to elucidate intricate shear behaviors. Furthermore, comparative analysis indicates the slightly superior performance of the Random Forest over the Decision Tree, highlighting the efficacy of ensemble methods in this context. Extending the exploration to include XGBoost and LightGBM, the study showcases their potential as accurate shear strength predictors. The performance of the models is validated through scatter plots and error distribution plots, confirming accurate shear strength predictions across various scenarios. This research contributes significantly to the advancement of structural engineering methodologies by highlighting the potential of ML to improve the accuracy of shear strength estimation. The findings not only underscore the exceptional performance of ML models but also provide valuable insights into their comparative effectiveness, paving the way for enhanced structural assessments in columns.

Keywords

Machine Learning, Random Forest, Reinforced concrete, Regression, Shear Strength

References

• A., W. K., & O., J. J. (1984). Influence of Reinforcement on RC Short Column Lateral Resistance. Journal of Structural Engineering, 110(1), 90–104. https://doi.org/10.1061/(ASCE)0733-9445(1984)110:1(90)
• Ahn, J.-M., & Shin, S.-W. (2007). An evaluation of ductility of high-strength reinforced concrete columns subjected to reversed cyclic loads under axial compression. Magazine of Concrete Research - MAG CONCR RES, 59, 29–44. https://doi.org/10.1680/macr.2007.59.1.29
• Babaee Tirkolaee, E., Goli, A., & Weber, G.-W. (2020). Fuzzy Mathematical Programming and Self-Adaptive Artificial Fish Swarm Algorithm for Just-in-Time Energy-Aware Flow Shop Scheduling Problem With Outsourcing Option. IEEE Transactions on Fuzzy Systems, 28(11), 2772–2783. https://doi.org/10.1109/TFUZZ.2020.2998174
• Belkacem, M. A., Bechtoula, H., Bourahla, N., & Belkacem, A. A. (2019). Effect of axial load and transverse reinforcements on the seismic performance of reinforced concrete columns. Frontiers of Structural and Civil Engineering, 13(4), 831–851. https://doi.org/10.1007/s11709-018-0513-3
• Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
• Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
• Dinh, N. H., Park, S.-H., & Choi, K.-K. (2019). Seismic performance of reinforced concrete columns retrofitted by textile-reinforced mortar jackets. Structure and Infrastructure Engineering. https://doi.org/10.1080/15732479.2019.1708958
• Emam, O., Younis Haggag, R. M., & Mohamed, N. (2021). A Survey Paper in Transportation Logistics based on Artificial Intelligence. International Journal of Supply and Operations Management, 8(4), 458–477. https://doi.org/10.22034/IJSOM.2021.4.6
• Eom, T.-S., Kang, S.-M., Park, H.-G., Choi, T.-W., & Jin, J.-M. (2014). Cyclic loading test for reinforced concrete columns with continuous rectangular and polygonal hoops. Engineering Structures, 67, 39–49. https://doi.org/10.1016/j.engstruct.2014.02.023
• Ghannoum, W., Sivaramakrishnan, B., Pujol, S., Catlin, A. C., Fernando, S., Yoosuf, N., & Wang, Y. (2012). ACI 369 rectangular column database. Network for Earthquake Engineering Simulation (Database). https://datacenterhub.org/dataviewer/view/neesdatabases:db/aci_369_rectangular_column_database/
• Goksu, C., Yilmaz, H., Chowdhury, S., Orakcal, K., & Ilki, A. (2014). The Effect of Lap Splice Length on the Cyclic Lateral Load Behavior of RC Members with Low-Strength Concrete and Plain Bars. Advances in Structural Engineering, 17, 639–658. https://doi.org/10.1260/1369-4332.17.5.639
• Hashemi Doulabi, H., & Khalilpourazari, S. (2023). Stochastic weekly operating room planning with an exponential number of scenarios. Annals of Operations Research, 328(1), 643–664. https://doi.org/10.1007/s10479-022-04686-4
• Ho, J. C. M. (2012). Experimental Tests on High-Strength Concrete Columns Subjected to Combined Medium Axial Load and Flexure. Advances in Structural Engineering, 15(8), 1359–1374. https://doi.org/10.1260/1369- 4332.15.8.1359 Hugo, R., André, F., & António, A. (2016). Behavior of Rectangular Reinforced-Concrete Columns under Biaxial Cyclic Loading and Variable Axial Loads. Journal of Structural Engineering, 142(1), 4015085. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001345
• Hwang, S.-K., & Yun, H.-D. (2004). Effects of transverse reinforcement on flexural behaviour of high-strength concrete columns. Engineering Structures, 26, 1–12. https://doi.org/10.1016/j.engstruct.2003.08.004
• Karbasi Arani, K., Di Ludovico, M., Marefat, M., Prota, A., & Manfredi, G. (2014). Lateral Response Evaluation of Old Type Reinforced Concrete Columns with Smooth Bars. Aci Structural Journal, 111. https://doi.org/10.14359/51686734
• Karbasi Arani, K., Marefat, M., Amrollahi-Biucky, A., & Khanmohammadi, M. (2013). Experimental seismic evaluation of old concrete columns reinforced by plain bars. The Structural Design of Tall and Special Buildings, 22. https://doi.org/10.1002/tal.686
• Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, 3149–3157, https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf.
• Khalilpourazari, S., & Doulabi, H. (2021). Using reinforcement learning to forecast the spread of COVID-19 in France. 1–8. https://doi.org/10.1109/ICAS49788.2021.9551174 Khalilpourazari, S., & Hashemi Doulabi, H. (2022). A flexible robust model for blood supply chain network design problem. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04673-9
• Khalilpourazari, S., Mirzazadeh, A., Weber, G.-W., & Pasandideh, S. H. R. (2020). A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process. Optimization, 69(1), 63–90. https://doi.org/10.1080/02331934.2019.1630625
• Khalilpourazari, S., & Pasandideh, S. (2016). Bi-objective optimization of multi-product EPQ model with backorders, rework process and random defective rate. https://10.1109/INDUSENG.2016.7519346.
• Lam, S., Wu, B., Wong, Y., Wang, Z. Y., Liu, Z., & Li, C. (2003). Drift Capacity of Rectangular Reinforced Concrete Columns with Low Lateral Confinement and High-Axial Load. Journal of Structural Engineering-Asce - J STRUCT ENG-ASCE, 129. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:6(733)
• Li, Y.-A., Huang, Y.-T., & Hwang, S.-J. (2014). Seismic Response of Reinforced Concrete Short Columns Failed in Shear. ACI Structural Journal, 111. https://doi.org/10.14359/51686780
• Li, Y., Cao, S., & Jing, D.-H. (2018). Concrete Columns Reinforced with High-Strength Steel Subjected to Reversed Cycle Loading. ACI Structural Journal, 115. https://doi.org/10.14359/51701296
• Liu, T., Wang, Z., Zeng, J., & Wang, J. (2021). Machine-learning-based models to predict shear transfer strength of concrete joints. Engineering Structures, 249, 113253. https://doi.org/https://doi.org/10.1016/j.engstruct.2021.113253
• Ma, L., Zhou, C., Lee, D., & Zhang, J. (2022). Prediction of axial compressive capacity of CFRP-confined concrete-filled steel tubular short columns based on XGBoost algorithm. Engineering Structures, 260, 114239. https://doi.org/https://doi.org/10.1016/j.engstruct.2022.114239
• Marefat, M. S., Khanmohammadi, M., Bahrani, M. K., & Goli, A. (2006). Experimental Assessment of Reinforced Concrete Columns with Deficient Seismic Details under Cyclic Load. Advances in Structural Engineering, 9(3), 337–347. https://doi.org/10.1260/136943306777641959
• Melo, J., Varum, H., & Rossetto, T. (2015). Experimental cyclic behaviour of RC columns with plain bars and proposal for Eurocode 8 formula improvement. Engineering Structures, 88, 22–36. https://doi.org/10.1016/j.engstruct.2015.01.033
• Mohammadi, M., & Khalilpourazari, S. (2017). Minimizing Makespan in a single machine scheduling problem with deteriorating jobs and learning effects. https://doi.org/10.1145/3056662.3056715
• Moslemi, S., Mirzazadeh, A., Weber, G.-W., & Sobhanallahi, M. A. (2021). Integration of neural network and AP-NDEA model for performance evaluation of sustainable pharmaceutical supply chain. OPSEARCH. https://doi.org/10.1007/s12597-021-00561-1
• Naser, M. Z. (2021). Mechanistically Informed Machine Learning and Artificial Intelligence in Fire Engineering and Sciences. Fire Technology, 1–44. https://doi.org/10.1007/s10694-020-01069-8
• Naser, M. Z., & Ciftcioglu, A. O. (2022). Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge. https://doi.org/10.48550/arxiv.2204.05311
• Naser, M. Z., & Çiftçioğlu, A. Ö. (2023). Revisiting Forgotten Fire Tests: Causal Inference and Counterfactuals for Learning Idealized Fire-Induced Response of RC Columns. Fire Technology, 59(4), 1761–1788. https://doi.org/10.1007/s10694-023-01405-8 9 Nguyen-Sy, T., Wakim, J., To, Q. D., Vu, M. N., Nguyen, T. D., & Nguyen, T. T. (2020). Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Construction and Building Materials, 260, 119757. https://doi.org/10.1016/j.conbuildmat.2020.119757
• Nguyen, H., Vu, T., Vo, T. P., & Thai, H. T. (2021). Efficient machine learning models for prediction of concrete strengths. Construction and Building Materials, 266, 120950. https://doi.org/10.1016/j.conbuildmat.2020.120950 Özyüksel Çiftçioğlu, A. (2023). RAGN-L: A Stacked Ensemble Learning Technique for Classification of Fire- Resistant Columns. Expert Systems with Applications, 240(November 2023), 122491. https://doi.org/10.1016/j.eswa.2023.122491
• Özyüksel Çiftçioğlu, A., & Naser, M. Z. (2022). Hiding in plain sight: What can interpretable unsupervised machine learning and clustering analysis tell us about the fire behavior of reinforced concrete columns? Structures, 40(April), 920–935. https://doi.org/10.1016/j.istruc.2022.04.076
• Park, H.-G., Yu, E.-J., & Choi, K.-K. (2012). Shear-strength degradation model for RC columns subjected to cyclic loading. Engineering Structures, 34, 187–197. https://doi.org/https://doi.org/10.1016/j.engstruct.2011.08.041
• Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251 Rajakarunakaran, S. A., Lourdu, A. R., Muthusamy, S., Panchal, H., Jawad Alrubaie, A., Musa Jaber, M., Ali, M.
• H., Tlili, I., Maseleno, A., Majdi, A., & Ali, S. H. M. (2022). Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques. Advances in Engineering Software, 173, 103267. https://doi.org/https://doi.org/10.1016/j.advengsoft.2022.103267
• Shi, Q., Ma, L., Wang, Q., Wang, B., & Yang, K. (2021). Seismic performance of square concrete columns reinforced with grade 600 MPa longitudinal and transverse reinforcement steel under high axial load. Structures, 32, 1955–1970. https://doi.org/10.1016/j.istruc.2021.03.110
• Van Rossum, G., & Drake Jr, F. L. (1995). Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam, https://ir.cwi.nl/pub/5008.
• Wong, H. F., & Kuang, J. S. (2014). Predicting shear strength of RC interior beam–column joints by modified rotating-angle softened-truss model. Computers & Structures, 133, 12–17. https://doi.org/https://doi.org/10.1016/j.compstruc.2013.11.008
• Woods, J., Kiousis, P., Ehsani, M., Saadatmanesh, H., & Fritz, W. (2007). Bending ductility of rectangular high strength concrete columns. Engineering Structures - ENG STRUCT, 29, 1783–1790. https://doi.org/10.1016/j.engstruct.2006.09.024
• Zavvar Sabegh, M. H., Mirzazadeh, A., Salehian, S., & Wilhelm Weber, G. (2014). A Literature Review on the Fuzzy Control Chart; Classifications & Analysis. International Journal of Supply and Operations Management, 1(2), 167–189. https://doi.org/10.22034/2014.2.03
• Zhang, J., Cai, R., Li, C., & Liu, X. (2020). Seismic behavior of high-strength concrete columns reinforced with high-strength steel bars. Engineering Structures, 218, 110861. https://doi.org/10.1016/j.engstruct.2020.110861
• Zhang, X., Akber, M. Z., & Zheng, W. (2021). Prediction of seven-day compressive strength of field concrete. Construction and Building Materials, 305, 124604. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2021.124604
• Zhang, Y., Zheng, S., Rong, X., Dong, L., & Zheng, H. (2019). Seismic Performance of Reinforced Concrete Short Columns Subjected to Freeze–Thaw Cycles. Applied Sciences, 9(13). https://doi.org/10.3390/app9132708
• Zhou, X., & Liu, J. (2010). Seismic behavior and shear strength of tubed RC short columns. Journal of Constructional Steel Research, 66(3), 385–397. https://doi.org/https://doi.org/10.1016/j.jcsr.2009.10.011