Prediction of Bidirectional Shear Strength of Rectangular RC Columns Subjected to Multidirectional Earthquake Actions for Collapse Prevention

Yingbo Pang, Iftikhar Azim, Momina Rauf, Muhammad Farjad Iqbal, Xinguang Ge, Muhammad Ashraf, Muhammad Atiq Ur Rahman Tariq and Anne W. M. Ng
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Yingbo Pang: College of Civil Engineering and Architecture, Guangxi Vocational Normal University, Nanning 530007, China
Iftikhar Azim: State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Momina Rauf: Department of Civil Engineering, Military College of Engineering, NUST, Risalpur 23200, Pakistan
Muhammad Farjad Iqbal: State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Xinguang Ge: State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Muhammad Ashraf: Department of Civil Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Swabi 23460, Pakistan
Muhammad Atiq Ur Rahman Tariq: Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne 8001, Australia
Anne W. M. Ng: Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne 8001, Australia

Sustainability, 2022, vol. 14, issue 11, 1-25

Abstract: The understanding of the effects of multidirectional loadings imposed on major load bearing elements such as reinforced concrete (RC) columns by seismic actions for collapse prevention is of utmost importance, and a few simplified models are available in the literature. In this study, the distinguishing features of two machine-learning (ML) methods, namely, multi expression programming (MEP) and adaptive neuro-fuzzy inference system (ANFIS) are exploited for the first time to develop eight novel prediction models (M1-to M4-MEP and M1-to M4-ANFIS) with different combinations of input parameters to predict the biaxial shear strength of RC columns ( V ). The performance of the developed models was assessed using various statistical indicators and by comparing them with the experimental values. Based on the statistical analysis of the developed models, M1-ANFIS and M1-MEP performed very well and exhibited the best overall efficiency of the studied ML methods. Simple mathematical formulations were also provided by the MEP algorithm for the prediction of V , using which the M1-MEP model was finalized based on its performance, accuracy, and generalization capability. A parametric analysis was also performed for the model to show that the mathematical formulation provided by MEP accurately represents the system under consideration and is imperative for prediction purposes. Based on its performance, the model can thus be recommended to update the current code provisions and engineering practices.

Keywords: evolutionary algorithms; predictive modelling; biaxial shear strength; RC columns (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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