Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms

Majid Khan; Adil Khan; Asad Ullah Khan; Muhammad Shakeel; Khalid Khan; Hisham Alabduljabbar; Taoufik Najeh; Yaser Gamil

doi:10.1016/j.heliyon.2023.e23375

Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms

Heliyon. 2023 Dec 7;10(1):e23375. doi: 10.1016/j.heliyon.2023.e23375. eCollection 2024 Jan 15.

Authors

Majid Khan¹, Adil Khan², Asad Ullah Khan³, Muhammad Shakeel³, Khalid Khan³, Hisham Alabduljabbar⁴, Taoufik Najeh⁵, Yaser Gamil⁶

Affiliations

¹ COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan.
² Department of Civil and Structural Engineering, University of Bradford, Bradford, West Yorkshire, BD7 1DP, UK.
³ Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan.
⁴ Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
⁵ Operation, Maintenance, and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Sweden.
⁶ Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.

Abstract

Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.

Keywords: Fiber-reinforced polymers; Flexural capacity; Gene expression programming; Machine learning; Multi-expression programming.