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Structural Engineering and Mechanics Volume 85, Number 3, February10 2023 , pages 315-335 DOI: https://doi.org/10.12989/sem.2023.85.3.315 |
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Machine learning model for predicting ultimate capacity of FRP-reinforced normal strength concrete structural elements |
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Selmi Abdellatif and Ali Raza
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| Abstract | ||
| Limited studies are available on the mathematical estimates of the compressive strength (CS) of glass fiberembedded polymer (glass-FRP) compressive elements. The present study has endeavored to estimate the CS of glass-FRP normal strength concrete (NSTC) compression elements (glass-FRP-NSTC) employing two various methodologies; mathematical modeling and artificial neural networks (ANNs). The dataset of 288 glass-FRP-NSTC compression elements was constructed from the various testing investigations available in the literature. Diverse equations for CS of glass-FRP-NSTC compression elements suggested in the previous research studies were evaluated employing the constructed dataset to examine their correctness. A new mathematical equation for the CS of glass-FRP-NSTC compression elements was put forwarded employing the procedures of curve-fitting and general regression in MATLAB. The newly suggested ANN equation was calibrated for various hidden layers and neurons to secure the optimized estimates. The suggested equations reported a good correlation among themselves and presented precise estimates compared with the estimates of the equations available in the literature with R2= 0.769, and R2 =0.9702 for the mathematical and ANN equations, respectively. The statistical comparison of diverse factors for the estimates of the projected equations also authenticated their high correctness for apprehending the CS of glass-FRP-NSTC compression elements. A broad parametric examination employing the projected ANN equation was also performed to examine the effect of diverse factors of the glass-FRP-NSTC compression elements. | ||
| Key Words | ||
| ANN equation; compression element; compressive strength; glass-FRP; mathematical equation | ||
| Address | ||
| Selmi Abdellatif: Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia; Civil Engineering, Laboratory, Ecole Nationale d'Ingénieurs de Tunis (ENIT), B.P. 37, Le belvédère 1002, Tunis, Tunisia Ali Raza: Department of Civil Engineering, University of Engineering and Technology Taxila, 47050, Pakistan | ||