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, Anne W.M. Ng

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    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.

    Original languageEnglish
    Article number3274
    Pages (from-to)1-15
    Number of pages15
    JournalSustainability (Switzerland)
    Issue number6
    Publication statusPublished - 2 Mar 2022


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