Evolutionary Algorithm-Based Modeling of Split Tensile Strength of Foundry Sand-Based Concrete

Tao Guan, Wang Shanku, Momina Rauf, Shahzeb Adil, Muhammad Farjad Iqbal, Muhammad Atiq Ur Rahman Tariq, Iftikhar Azim and Anne W. M. Ng
Additional contact information
Tao Guan: Xinyang Vocational and Technical College, Xinyang 464000, China
Wang Shanku: Xinyang Vocational and Technical College, Xinyang 464000, China
Momina Rauf: Department of Civil Engineering, Military College of Engineering, NUST, Risalpur 23200, Pakistan
Shahzeb Adil: Department of Civil Engineering, National University of Computer and Emerging Sciences (FAST-NUCES), Lahore 54770, Pakistan
Muhammad Farjad Iqbal: State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Muhammad Atiq Ur Rahman Tariq: College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
Iftikhar Azim: State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Anne W. M. Ng: College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia

Sustainability, 2022, vol. 14, issue 6, 1-15

Abstract: Foundry sand (FS) is produced as a waste material by metal casting foundries. It is being utilized as an alternative to fine aggregates for developing sustainable concrete. In this paper, an artificial intelligence technique, i.e., gene expression programming (GEP) has been implemented to empirically formulate prediction models for split tensile strength (ST) of concrete containing FS. For this purpose, an extensive experimental database has been collated from the literature and split up into training, validation, and testing sets for modeling purposes. ST is modeled as a function of water-to-cement ratio, percentage of FS, and FS-to-cement content ratio. The reliability of the proposed expression is validated by conducting several statistical and parametric analyses. The modeling results depicted that the prediction model is robust and accurate with a high generalization capability. The availability of reliable formulation to predict strength properties can promote the utilization of foundry industry waste in the construction sector, promoting green construction and saving time and cost incurred during experimental testing.

Keywords: sustainable concrete; foundry sand; prediction model; gene expression programming; split tensile strength; parametric analysis (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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