Research Articles | Challenge Journal of Structural Mechanics

Ultimate drift ratio prediction of steel plate shear wall systems: a machine learning approach

Muhammed Gürbüz, İlker Kazaz


DOI: https://doi.org/10.20528/cjsmec.2024.02.001
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Abstract


Predicting the ultimate drift ratio of steel plate shear wall (SPSW) systems is important for ensuring the structural integrity and performance of these systems under lateral loads. In this study, machine learning models are developed for predicting the ultimate drift ratio based on various design parameters using data from previous research on SPSW systems. These design parameters include the panel aspect ratio, column flexibility parameter, axial load ratio, web plate thickness and stiffness of horizontal and vertical boundary elements. A range of machine learning models is considered, including Random Forest, Lasso, Gradient Boosting, XGBoost, Adaboost, Artificial Neural Network and a stacked regressor. The models are trained and evaluated using data from 292 different SPSW models, and their performance is compared based on the R-squared value, root mean squared error (RMSE), and evaluation time. The results of this study demonstrate the effectiveness of machine learning techniques for predicting the ultimate drift ratio of SPSW systems. The results of this study show that machine learning techniques effectively predict the ultimate drift ratio of SPSW systems. Among the models considered, the ANN model achieved the highest R2 value, with a value of 0.94.


Keywords


steel plate shear walls; finite element analysis; machine learning; artificial neural network; stacked regressor

References


Adeli H, Yeh C (1989). Perceptron learning in engineering design model. Microcomputers in Civil Engineering, 4, 247–256.

ANSYS (2016). Academic Research Mechanical, Release 17.2, Help System; ANSYS. Inc.: Canonsburg, PA, USA.

Breiman L (2001). Random forests. Machine Learning, 45(1), 5–32.

Chen T, Guestrin C (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu, 785–794.

Deep Learning Toolbox - MATLAB. (n.d.). Retrieved January 11, 2023, from https://www.mathworks.com/products/deep-learning.html

Freund Y, Schapire R. E (1997). A Decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.

Gürbüz M, Kazaz İ (2022a). A numerical investigation on the limitations of design equations for steel plate shear walls. Teknik Dergi, 33(5), 12677–12708.

Gürbüz M, Kazaz İ (2022b). Numerical evaluation on the steel plate shear wall design parameters for improved cyclic behavior. International Journal of Steel Structures, 22(2), 409–429.

Hajela P, Berke L (1991). Neurobiological computational models in structural analysis and design. Computers and Structures, 41(4), 657–667.

Inel M (2007). Modeling ultimate deformation capacity of RC columns using artificial neural networks. Engineering Structures, 29(3), 329–335.

Kalman Šipoš T, Sigmund V, Hadzima-Nyarko M (2013). Earthquake performance of infilled frames using neural networks and experimental database. Engineering Structures, 51, 113–127.

Li CH, Tsai KC (2008). Experimental responses of four 2-story narrow steel plate shear walls. Proceedings of the 2008 Structures Congress, vol. 314, Vancouver, Canada.

Luo H, Paal SG (2019). A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments. Computer-Aided Civil and Infrastructure Engineering, 34(11), 935–950.

Mahfuz Ud Darain K, Shamshirband S, Jumaat MZ, Obaydullah M (2015). Adaptive neuro fuzzy prediction of deflection and cracking behavior of NSM strengthened RC beams. Construction and Building Materials, 98, 276–285.

Nguyen HD, Dao ND, Shin M (2021). Prediction of seismic drift responses of planar steel moment frames using artificial neural network and extreme gradient boosting. Engineering Structures, 242, 112518.

Scikit-learn: machine learning in Python ‒ scikit-learn 1.2.0 documentation. (n.d.). Retrieved January 10, 2023, from https://scikit-learn.org/stable/index.html

Sun H, Burton HV, Huang H (2021). Machine learning applications for building structural design and performance assessment: State-of-the-art review. Journal of Building Engineering, 33, 101816.

Thai HT (2022). Machine learning for structural engineering: A state-of-the-art review. Structures, 38, 448–491.

Tibshiranit R (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.

Wu X, Ghaboussi J, Garrett JH (1992). Use of neural networks in detection of structural damage. Computers and Structures, 42(4), 649–659.

Xie Y, Ebad Sichani M, Padgett JE, DesRoches R (2020). The promise of implementing machine learning in earthquake engineering: A state-of-the-art review. Earthquake Spectra, 36(4), 1769–1801.

Ying X (2019). An overview of overfitting and its solutions. Journal of Physics: Conference Series, 1168(2).


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