Machine Learning Prediction Model for Shear Capacity of FRP-RC Slender and Deep Beams

Ahmad Tarawneh (), Abdullah Alghossoon, Eman Saleh, Ghassan Almasabha, Yasmin Murad, Mahmoud Abu-Rayyan and Ahmad Aldiabat
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Ahmad Tarawneh: Civil Engineering Department, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Abdullah Alghossoon: Civil Engineering Department, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Eman Saleh: Civil Engineering Department, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Ghassan Almasabha: Civil Engineering Department, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Yasmin Murad: Civil Engineering Department, The University of Jordan, Amman P.O. Box 11942, Jordan
Mahmoud Abu-Rayyan: Civil Engineering Department, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Ahmad Aldiabat: Civil Engineering Department, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan

Sustainability, 2022, vol. 14, issue 23, 1-18

Abstract: FPR reinforcing bars have emerged as a promising alternative to steel bars in construction, especially in corrosive environments. Literature includes several shear strength models proposed for FRP-RC members. This study presents a detailed evaluation of design shear models proposed by researchers and design codes. The evaluation was conducted through an extensive surveyed database of 388 FRP-RC beams without shear reinforcement tested in shear. Gene expression programming (GEP) has been utilized in this study to develop accurate design models for the shear capacity of slender and deep FRP-RC beams. Parameters used in the models are concrete compressive strength ( f’ c ), section depth ( d ), section width ( b ), modular ratio ( n ), reinforcement ratio ( ρ f ), shear span-to-depth ratio ( a / d ). The proposed model for slender beams resulted in an average tested-to-predicted ratio of 0.98 and a standard deviation of 0.21, while the deep beams model resulted in an average tested-to-predicted ratio of 1.03 and a standard deviation of 0.29. For deep beams, the model provided superior accuracy over all models. However, this can be attributed to the fact that the investigated models were not intended for deep beams. The deep beams model provides a simple method compared to the strut-and-tie method.

Keywords: FRP bars; shear strength; machine learning; gene expression; deep beams; slender beams (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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