Shear capacity prediction of squat flanged walls using machine learning approach with CatBoost and CTGAN
Khalid Saqer ALOTAIBI
Abstract. Reinforced concrete (RC) walls serve an important role in providing lateral strength and stiffness for structures against seismic forces and wind loads. Squat walls, defined as having a shear span ratio less than or equal to two, are commonly used in building construction. Among squat wall designs, flanged walls have gained popularity due to architectural benefits. However, the seismic behavior of flanged RC squat walls is not fully understood due to complexities introduced by their cross-sectional shape and varying shear span ratios. Current structural design codes aim to capture wall behavior but have limitations in accurately modeling flanged squat walls. Previous experimental work has enhanced comprehension, but key aspects remain unsolved, such as precise prediction of stiffness and effective flange length. Data-driven machine learning models show promise for predicting engineering properties of composite materials like concrete. However, limited publicly available data hinders developing highly predictive computational models. This study addresses prior data limitations by using conditional tabular generative adversarial network (CTGAN) to synthesize additional tabular data. CTGAN, an adversarial learning method for tabular datasets, can generate realistic synthetic rows conditioned on discrete features. Here, CTGAN was trained on a 152-specimen experimental database to synthesize 5,000 data points. The synthesized data were then used to train a CatBoost gradient boosting algorithm for predicting shear capacity of flanged squat walls. The model achieved a high R-squared value of 0.878 when tested on 152 unseen experimental specimens, demonstrating accurate prediction of shear capacity in real data.
Keywords
Squat Flanged Reinforced Concrete Wall, Shear Strength, CTGAN, CatBoost Machine Learning
Published online 2/25/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Khalid Saqer ALOTAIBI, Shear capacity prediction of squat flanged walls using machine learning approach with CatBoost and CTGAN, Materials Research Proceedings, Vol. 48, pp 40-49, 2025
DOI: https://doi.org/10.21741/9781644903414-5
The article was published as article 5 of the book Civil and Environmental Engineering for Resilient, Smart and Sustainable Solutions
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] C.K. Gulec, A.S. Whittaker, B. Stojadinovic, Shear strength of squat rectangular reinforced concrete walls, ACI Structural Journal 105 (2008) 488. https://doi.org/10.14359/19863
[2] C.K. Gulec, A.S. Whittaker, B. Stojadinovic, Peak shear strength of squat reinforced concrete walls with boundary barbells or flanges, ACI Structural Journal 106 (2009) 368. https://doi.org/10.14359/56501
[3] W. Kassem, A. Elsheikh, Estimation of shear strength of structural shear walls, Journal of Structural Engineering 136 (2010) 1215-1224. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000218
[4] B. Li, W. Xiang, Effective stiffness of squat structural walls, Journal of Structural Engineering 137 (2011) 1470-1479. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000386
[5] B. Luna, A. Whittaker, J. Rivera, Seismic behavior of low aspect ratio reinforced concrete shear walls, 2013.
[6] J. Ma, B. Li, Seismic behavior of L-shaped RC squat walls under various lateral loading directions, Journal of Earthquake Engineering 23 (2019) 422-443. https://doi.org/10.1080/13632469.2017.1326424
[7] D. Palermo, F.J. Vecchio, H. Solanki, Behavior of three-dimensional reinforced concrete shear walls, ACI Structural Journal 99 (2002) 81-89. https://doi.org/10.14359/11038
[8] J. Ma, B. Li, Experimental and analytical studies on H-shaped reinforced concrete squat walls, 2018. https://doi.org/10.14359/51701144
[9] J. Ma, M. Wang, C. Wang, X. Yu, B. Li, Database of flanged reinforced concrete squat walls and its utilization based on machine learning, Structures 58 (2023) 105649. https://doi.org/10.1016/j.istruc.2023.105649
[10] J. Ma, Z. Zhang, B. Li, Experimental assessment of T-shaped reinforced concrete squat walls, 2018. https://doi.org/10.14359/51701294
[11] Z. Zhang, B. Li, Effective stiffness of non-rectangular reinforced concrete structural walls, Journal of Earthquake Engineering 22 (2018) 382-403. https://doi.org/10.1080/13632469.2016.1224744
[12] M. Hassan, S. El-Tawil, Tension flange effective width in reinforced concrete shear walls, Structural Journal 100 (2003) 349-356. https://doi.org/10.14359/12610
[13] Z. Zhang, B. Li, Shear lag effect in tension flange of RC walls with flanged sections, Engineering Structures 143 (2017) 64-76. https://doi.org/10.1016/j.engstruct.2017.04.017
[14] M. Almustafa, M. Nehdi, Machine learning model for predicting structural response of RC slabs exposed to blast loading, Engineering Structures 221 (2020) 111109. https://doi.org/10.1016/j.engstruct.2020.111109
[15] D.C. Feng, B. Cetiner, M.R. Azadi Kakavand, E. Taciroglu, Data-driven approach to predict the plastic hinge length of reinforced concrete columns and its application, Journal of Structural Engineering 147 (2021) 04020332. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002852
[16] D.C. Feng, Z.T. Liu, X.D. Wang, Z.M. Jiang, S.X. Liang, Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm, Advanced Engineering Informatics 45 (2020) 101126. https://doi.org/10.1016/j.aei.2020.101126
[17] D.C. Feng, W.J. Wang, S. Mangalathu, E. Taciroglu, Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls, Journal of Structural Engineering 147 (2021) 04021173. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003115
[18] B. Fu, D.C. Feng, A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams, Journal of Building Engineering 36 (2021) 102118. https://doi.org/10.1016/j.jobe.2020.102118
[19] S.H. Hwang, S. Mangalathu, J. Shin, J.S. Jeon, Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames, Journal of Building Engineering 34 (2021) 101905. https://doi.org/10.1016/j.jobe.2020.101905
[20] B. Keshtegar, M.L. Nehdi, N.T. Trung, R. Kolahchi, Predicting load capacity of shear walls using SVR-RSM model, Applied Soft Computing 112 (2021) 107739. https://doi.org/10.1016/j.asoc.2021.107739
[21] S. Mangalathu, H. Jang, S.H. Hwang, J.S. Jeon, Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls, Engineering Structures 208 (2020) 110331. https://doi.org/10.1016/j.engstruct.2020.110331
[22] D.D. Nguyen, V.L. Tran, H.H. Da, V.Q. Nguyen, T.H. Lee, A machine learning-based formulation for predicting shear capacity of squat flanged RC walls, Structures (2021) 1734-1747. https://doi.org/10.1016/j.istruc.2020.12.054
[23] J. Rahman, K.S. Ahmed, N.I. Khan, K. Islam, S. Mangalathu, Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach, Engineering Structures 233 (2021) 111743. https://doi.org/10.1016/j.engstruct.2020.111743
[24] H. Zhang, X. Cheng, Y. Li, X. Du, Prediction of failure modes, strength, and deformation capacity of RC shear walls through machine learning, Journal of Building Engineering 50 (2022) 104145. https://doi.org/10.1016/j.jobe.2022.104145
[25] N. Mohammed, A. Asiz, M.A. Khasawneh, H. Mewada, T. Sultana, Machine learning and RSM-CCD analysis of green concrete made from waste water plastic bottle caps: Towards performance and optimization, Mechanics of Advanced Materials and Structures (2023) 1-9. https://doi.org/10.1080/15376494.2023.2238220
[26] N. Mohammed, P. Palaniandy, F. Shaik, B. Deepanraj, H. Mewada, Statistical analysis by using soft computing methods for seawater biodegradability using ZnO photocatalyst, Environmental Research 227 (2023) 115696. https://doi.org/10.1016/j.envres.2023.115696
[27] N. Mohammed, P. Palaniandy, F. Shaik, H. Mewada, D. Balakrishnan, Comparative studies of RSM Box-Behnken and ANN-Anfis fuzzy statistical analysis for seawater biodegradability using TiO2 photocatalyst, Chemosphere 314 (2023) 137665. https://doi.org/10.1016/j.chemosphere.2022.137665
[28] G. Almasabha, K.F. Al-Shboul, A. Shehadeh, O. Alshboul, Machine learning-based models for predicting the shear strength of synthetic fiber reinforced concrete beams without stirrups, Structures (2023) 299-311. https://doi.org/10.1016/j.istruc.2023.03.170
[29] T. Jayasinghe, B. wei Chen, Z. Zhang, X. Meng, Y. Li, T. Gunawardena, S. Mangalathu, P. Mendis, Data-driven shear strength predictions of recycled aggregate concrete beams with/without shear reinforcement by applying machine learning approaches, Construction and Building Materials 387 (2023) 131604. https://doi.org/10.1016/j.conbuildmat.2023.131604
[30] K. Le Nguyen, H.T. Trinh, T.T. Nguyen, H.D. Nguyen, Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications, Expert Systems with Applications 230 (2023) 120649. https://doi.org/10.1016/j.eswa.2023.120649
[31] J. Rahman, P. Arafin, A.M. Billah, Machine learning models for predicting concrete beams shear strength externally bonded with FRP, Structures (2023) 514-536. https://doi.org/10.1016/j.istruc.2023.04.069
[32] M.S. Sandeep, K. Tiprak, S. Kaewunruen, P. Pheinsusom, W. Pansuk, Shear strength prediction of reinforced concrete beams using machine learning, Structures (2023) 1196-1211. https://doi.org/10.1016/j.istruc.2022.11.140
[33] Z.M. Yaseen, Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study, Scientific Reports 13 (2023) 1723. https://doi.org/10.1038/s41598-023-27613-4
[34] M. Ye, L. Li, D.Y. Yoo, H. Li, C. Zhou, X. Shao, Prediction of shear strength in UHPC beams using machine learning-based models and SHAP interpretation, Construction and Building Materials 408 (2023) 133752. https://doi.org/10.1016/j.conbuildmat.2023.133752
