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Improved data-driven models for estimating shear capacity of squat rectangular reinforced concrete walls

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Abstract

Reinforced concrete (RC) shear walls are very commonly used in buildings and nuclear power plants. Shear strength is one of the critical parameters in the design of RC walls, especially considering the influence of horizontal loads such as wind or earthquakes. The objective of this paper is to build machine learning (ML) models to predict the shear capacity of rectangular RC squat walls. A dataset of 312 experimental results in previous studies were collected and used for training ML models. Six ML models including Artificial neural network-Levenberg Marquardt (ANN-LM), Artificial neural network-Bayesian regularization (ANN-BR), Artificial neural network-Gene algorithm (ANN-GA), Adaptive neuro fuzzy inference system (ANFIS), Random Forest (RF), and Gradient boosting regression tree (GBRT), were developed to predict the shear strength of RC walls. The prediction results of the proposed ML models were compared with that from eight empirical formulas in design standards and published studies. From the comparison results, the RF and GBRT models predicted the shear capacity of RC walls much more accurately than existing formulas. Furthermore, a graphical user interface has been established based on an efficient ML model to facilitate the actual design process of rectangular RC short walls.

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References

  • ACI-318 (2014). ACI 318–14: Building Code Requirements for Structural Concrete and Commentary. American Concrete Institution Indianapolis, IN, USA.

  • Adorno-Bonilla, C.M. (2016). Shear strength and displacement capacity of squat reinforced concrete shear walls. (UNIVERSITY OF PUERTO RICO MAYAGÜEZ CAMPUS).

  • ASCE, SEI-43-05. (2005). Seismic design criteria for structures, Systems, and Components in Nuclear Facilities. American Society of Civil Engineers.

    Google Scholar 

  • Barda, F., Hanson, J. M., & Corley, W. G. (1977). Shear strength of low-rise walls with boundary elements. Special Publication, 53, 149–202.

    Google Scholar 

  • Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.

    Article  Google Scholar 

  • Burden, F., & Winkler, D. (2008). Bayesian regularization of neural networks. In D. J. Livingstone (Ed.), Artificial neural networks. Methods in molecular biology™ (Vol. 458, pp. 23–42). Humana Press. https://doi.org/10.1007/978-1-60327-101-1_3.

    Chapter  Google Scholar 

  • Chatterjee, S., Sarkar, S., Hore, S., Dey, N., Ashour, A. S., & Balas, V. E. (2017). Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Computing and Applications, 28, 2005–2016.

    Article  Google Scholar 

  • Chen, T., and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. pp. 785–794.

  • Chen, X., Fu, J., Yao, J., & Gan, J. (2018). Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model. Engineering with Computers, 34, 367–383.

    Article  Google Scholar 

  • Chou, J.-S., Liu, C.-Y., Prayogo, H., Khasani, R. R., Gho, D., & Lalitan, G. G. (2022). Predicting nominal shear capacity of reinforced concrete wall in building by metaheuristics-optimized machine learning. Journal of Building Engineering, 61, 105046.

    Article  Google Scholar 

  • EN-1998–1 (2004). Eurocode 8: Design of Structures for Earthquake Resistance - Part 1: General Rules. Seismic Actions and Rules for Buildings.

  • Falcone, R., Lima, C., & Martinelli, E. (2020). Soft computing techniques in structural and earthquake engineering: A literature review. Engineering Structures, 207, 110269.

    Article  Google Scholar 

  • Farzinpour, A., Dehcheshmeh, E. M., Broujerdian, V., Esfahani, S. N., & Gandomi, A. H. (2023). Efficient boosting-based algorithms for shear strength prediction of squat RC walls. Case Studies in Construction Materials, 18, e01928.

    Article  Google Scholar 

  • Gulec, C. K., & Whittaker, A. S. (2011). Empirical equations for peak shear strength of low aspect ratio reinforced concrete walls. ACI Structural Journal, 108, 80–89.

    Google Scholar 

  • Gulec, C. K., Whittaker, A. S., & Stojadinovic, B. (2008). Shear strength of squat rectangular reinforced concrete walls. ACI Structural Journal, 105, 488.

    Google Scholar 

  • Holland, J. H. (1992). Genetic Algorithms. Scientific American, 267, 66–73.

    Article  ADS  Google Scholar 

  • Jang, J.-S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665–685.

    Article  Google Scholar 

  • Kassem, W. (2015). Shear strength of squat walls: A strut-and-tie model and closed-form design formula. Engineering Structures, 84, 430–438.

    Article  Google Scholar 

  • Kaveh, A., Dadras Eslamlou, A., Javadi, S., & Geran Malek, N. (2021). Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders. Acta Mechanica, 232, 921–931.

    Article  Google Scholar 

  • Kaveh, A., Eskandari, A., and Movasat, M. (2023). Buckling resistance prediction of high-strength steel columns using metaheuristic-trained artificial neural networks. (Elsevier), pp. 104853.

  • Kaveh, A., and Khavaninzadeh, N. (2023). Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. (Elsevier), pp. 256–272.

  • Keshtegar, B., Nehdi, M. L., Trung, N.-T., & Kolahchi, R. (2021). Predicting load capacity of shear walls using SVR–RSM model. Applied Soft Computing, 112, 107739.

    Article  Google Scholar 

  • Mangalathu, S., Jang, H., Hwang, S.-H., & Jeon, J.-S. (2020). Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls. Engineering Structures, 208, 110331.

    Article  Google Scholar 

  • Moradi, M. J., & Hariri-Ardebili, M. A. (2019). Developing a library of shear walls database and the neural network based predictive meta-model. Applied Sciences, 9, 2562.

    Article  Google Scholar 

  • Nguyen, D.-D., Tran, V.-L., Ha, D.-H., Nguyen, V.-Q., & Lee, T.-H. (2021). A machine learning-based formulation for predicting shear capacity of squat flanged RC walls. Structures, 29, 1734–1747.

    Article  Google Scholar 

  • Ranganathan, A. (2004). The levenberg-marquardt algorithm. Tutoral on LM Algorithm, 11, 101–110.

    Google Scholar 

  • Sánchez-Alejandre, A., & Alcocer, S. M. (2010). Shear strength of squat reinforced concrete walls subjected to earthquake loading—trends and models. Engineering Structures, 32, 2466–2476.

    Article  Google Scholar 

  • Tariq, M., Khan, A., Ullah, A., Zamin, B., Kashyzadeh, K. R., & Ahmad, M. (2022). Gene Expression Programming for Estimating Shear Strength of RC Squat Wall. Buildings, 12, 918.

    Article  Google Scholar 

  • Thai, H.-T. (2022). Machine learning for structural engineering: A state-of-the-art review. (Elsevier), pp. 448–491.

  • Wood, S. L. (1990). Shear strength of low-rise reinforced concrete walls. Structural Journal, 87, 99–107.

    Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Vinh University, Vinh, 461010, Vietnam

    Trong-Ha Nguyen & Duy-Duan Nguyen

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  1. Trong-Ha Nguyen
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  2. Duy-Duan Nguyen
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Contributions

T-HN: conceptualization, software, visualization, writing—original draft. D-DN: methodology, formal analysis, validation, writing—original draft, writing—review & editing, supervision.

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Correspondence to Duy-Duan Nguyen.

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Nguyen, TH., Nguyen, DD. Improved data-driven models for estimating shear capacity of squat rectangular reinforced concrete walls. Asian J Civ Eng 25, 2729–2742 (2024). https://doi.org/10.1007/s42107-023-00941-6

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