Gene Expression Programming for Estimating Shear Strength of RC Squat Wall
Moiz Tariq,
Azam Khan,
Asad Ullah,
Bakht Zamin,
Kazem Reza Kashyzadeh,
Mahmood Ahmad
Affiliations
Moiz Tariq
NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering, National University of Science and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan
Azam Khan
NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering, National University of Science and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan
Asad Ullah
NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering, National University of Science and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan
Bakht Zamin
Civil Engineering Department, CECOS University of IT and Emerging Sciences, Peshawar 25000, Pakistan
Kazem Reza Kashyzadeh
Department of Transport, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, Moscow 117198, Russia
Mahmood Ahmad
Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan
The flanged, barbell, and rectangular squat reinforced concrete (RC) walls are broadly used in low-rise commercial and highway under and overpasses. The shear strength of squat walls is the major design consideration because of their smaller aspect ratio. Most of the current design codes or available published literature provide separate sets of shear capacity equations for flanged, barbell, and rectangular walls. Also, a substantial scatter exists in the predicted shear capacity due to a large discrepancy in the test data. Thus, this study aims to develop a single gene expression programming (GEP) expression that can be used for predicting the shear strength of these three cross-sectional shapes based on a dataset of 646 experiments. A total of thirteen influencing parameters are identified to contrive this efficient empirical compared to several shear capacity equations. Owing to the larger database, the proposed model shows better performance based on the database analysis results and compared with 9 available empirical models.