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Article

Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete

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Department of Civil and Environmental Engineering, College of Engineering, King Faisal University (KFU), P.O. Box 380, Al-Hofuf 31982, Saudi Arabia
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Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
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Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
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Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bander Seri Iskandar 32610, Malaysia
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Department of Construction of Unique Buildings and Constructions, Peter the Great St. Petersburg Polytechnic University, 195291 St. Petersburg, Russia
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Authors to whom correspondence should be addressed.
Academic Editor: Łukasz Sadowski
Materials 2021, 14(19), 5659; https://doi.org/10.3390/ma14195659
Received: 21 August 2021 / Revised: 23 September 2021 / Accepted: 25 September 2021 / Published: 28 September 2021
(This article belongs to the Special Issue Artificial Intelligence for Cementitious Materials)
The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been used to propose models for the compressive strength (fc), splitting tensile strength (fSTS), and flexural strength (fFS) of sustainable bagasse ash concrete (BAC). The training and testing of the proposed models have been accomplished by developing a reliable and comprehensive database from published literature. Concrete specimens with varying proportions of sugarcane bagasse ash (BA), as a partial replacement of cement, were prepared, and the developed models were validated by utilizing the results obtained from the tested BAC. Different statistical tests evaluated the accurateness of the models, and the results were cross-validated employing a k-fold algorithm. The modeling results achieve correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) above 0.8 each with relative root mean squared error (RRMSE) and objective function (OF) less than 10 and 0.2, respectively. The MEP model leads in providing reliable mathematical expression for the estimation of fc, fSTS and fFS of BA concrete, which can reduce the experimental workload in assessing the strength properties. The study’s findings indicated that MEP-based modeling integrated with experimental testing of BA concrete and further cross-validation is effective in predicting the strength parameters of BA concrete. View Full-Text
Keywords: multigene expression programming; experimental investigation; multiphysics models; machine learning; agricultural waste; sustainability; cross-validation multigene expression programming; experimental investigation; multiphysics models; machine learning; agricultural waste; sustainability; cross-validation
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MDPI and ACS Style

Amin, M.N.; Khan, K.; Aslam, F.; Shah, M.I.; Javed, M.F.; Musarat, M.A.; Usanova, K. Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete. Materials 2021, 14, 5659. https://doi.org/10.3390/ma14195659

AMA Style

Amin MN, Khan K, Aslam F, Shah MI, Javed MF, Musarat MA, Usanova K. Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete. Materials. 2021; 14(19):5659. https://doi.org/10.3390/ma14195659

Chicago/Turabian Style

Amin, Muhammad Nasir, Kaffayatullah Khan, Fahid Aslam, Muhammad Izhar Shah, Muhammad Faisal Javed, Muhammad Ali Musarat, and Kseniia Usanova. 2021. "Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete" Materials 14, no. 19: 5659. https://doi.org/10.3390/ma14195659

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