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Article

Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

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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, College of Engineering, King Faisal University (KFU), P.O. Box 380, Al-Hofuf 31982, Saudi Arabia
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Civil Engineering Department, Qurtuba University of Science and Information Technology, Khyber Pakhtunkhwa 29050, Pakistan
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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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Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
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Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany
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School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
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John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
*
Authors to whom correspondence should be addressed.
Academic Editors: Petr Hájek, Harald S. Müller and Domenico Asprone
Sustainability 2021, 13(5), 2867; https://doi.org/10.3390/su13052867
Received: 28 January 2021 / Revised: 25 February 2021 / Accepted: 2 March 2021 / Published: 6 March 2021
(This article belongs to the Special Issue Sustainable Concrete Structures)
The waste disposal crisis and development of various types of concrete simulated by the construction industry has encouraged further research to safely utilize the wastes and develop accurate predictive models for estimation of concrete properties. In the present study, sugarcane bagasse ash (SCBA), a by-product from the agricultural industry, was processed and used in the production of green concrete. An advanced variant of machine learning, i.e., multi expression programming (MEP), was then used to develop predictive models for modeling the mechanical properties of SCBA substitute concrete. The most significant parameters, i.e., water-to-cement ratio, SCBA replacement percentage, amount of cement, and quantity of coarse and fine aggregate, were used as modeling inputs. The MEP models were developed and trained by the data acquired from the literature; furthermore, the modeling outcome was validated through laboratory obtained results. The accuracy of the models was then assessed by statistical criteria. The results revealed a good approximation capacity of the trained MEP models with correlation coefficient above 0.9 and root means squared error (RMSE) value below 3.5 MPa. The results of cross-validation confirmed a generalized outcome and the resolved modeling overfitting. The parametric study has reflected the effect of inputs in the modeling process. Hence, the MEP-based modeling followed by validation with laboratory results, cross-validation, and parametric study could be an effective approach for accurate modeling of the concrete properties. View Full-Text
Keywords: multi expression programming; agricultural by-products; k-fold cross-validation; sustainable materials; artificial intelligence; materials design; sustainable concrete; machine learning; sustainable construction; big data multi expression programming; agricultural by-products; k-fold cross-validation; sustainable materials; artificial intelligence; materials design; sustainable concrete; machine learning; sustainable construction; big data
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MDPI and ACS Style

Shah, M.I.; Amin, M.N.; Khan, K.; Niazi, M.S.K.; Aslam, F.; Alyousef, R.; Javed, M.F.; Mosavi, A. Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete. Sustainability 2021, 13, 2867. https://doi.org/10.3390/su13052867

AMA Style

Shah MI, Amin MN, Khan K, Niazi MSK, Aslam F, Alyousef R, Javed MF, Mosavi A. Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete. Sustainability. 2021; 13(5):2867. https://doi.org/10.3390/su13052867

Chicago/Turabian Style

Shah, Muhammad Izhar, Muhammad Nasir Amin, Kaffayatullah Khan, Muhammad Sohaib Khan Niazi, Fahid Aslam, Rayed Alyousef, Muhammad Faisal Javed, and Amir Mosavi. 2021. "Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete" Sustainability 13, no. 5: 2867. https://doi.org/10.3390/su13052867

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