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Prediction of Rapid Chloride Penetration Resistance of Metakaolin Based Concrete Using Multi-Expression Programming

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This study investigates the resistance of concrete to Rapid Chloride ions Penetration (RCP) as an indirect measure of the concrete’s durability. The RCP resistance of concrete is modelled in multi-expression programming approach using different input variables, such as, age of concrete, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength (CS) of concrete. The parametric investigation was carried out by varying the hyperparameters, i.e., number of subpopulations N sub, subpopulation size S size, crossover probability C prob, mutation probability M prob, tournament size T size, code length C leng, and number of generations N gener to get an optimum model. The performance of all the 29 number of trained models were assessed by comparing mean absolute error (MAE) values. The optimum model was obtained for N sub = 50, S size = 100, C prob = 0.9, M prob = 0.01, T size = 9, C leng = 100, and N gener = 300 with MAE of 279.17 in case of training (TR) phase, whereas 301.66 for testing (TS) phase. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R and R 2 values equaling 0.96 and 0.93 (for the TR phase), and 0.92 and 0.90 (for the TS phase), respectively. Similarly, parametric and sensitivity analyses revealed that the RCP resistance is governed by the age of concrete, amount of binder, concrete CS, and aggregate quantity in the concrete mix. Among all the input variables, the RCP resistance sharply increased within the first 28 days age of the concrete specimen and similarly plummeted with increasing the quantity of fine aggregate, thus validating the model results.

Keywords: Metakaolin; Multi Expression Programming; Parametric Analysis; Rapid Chloride Ions Penetration; Sensitivity Analysis

Document Type: Research Article

Affiliations: 1: Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia 2: Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan 3: Department of Chemical Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan 4: Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Publication date: August 1, 2022

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  • Science of Advanced Materials (SAM) is an interdisciplinary peer-reviewed journal consolidating research activities in all aspects of advanced materials in the fields of science, engineering and medicine into a single and unique reference source. SAM provides the means for materials scientists, chemists, physicists, biologists, engineers, ceramicists, metallurgists, theoreticians and technocrats to publish original research articles as reviews with author's photo and short biography, full research articles and communications of important new scientific and technological findings, encompassing the fundamental and applied research in all latest aspects of advanced materials.
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