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Concept Paper

Random Forest Modeling for Fly Ash-Calcined Clay Geopolymer Composite Strength Detection

1
Department of Civil Engineering, Institute of Engineering & Technology, GLA University, Mathura 281406, India
2
Department of Mechanical Engineering, Institute of Engineering & Technology, GLA University, Mathura 281406, India
*
Author to whom correspondence should be addressed.
Academic Editors: Swadesh Kumar Singh, Suresh Kumar Tummala, Satyanarayana Kosaraju and Julfikar Haider
J. Compos. Sci. 2021, 5(10), 271; https://doi.org/10.3390/jcs5100271
Received: 30 July 2021 / Revised: 29 August 2021 / Accepted: 7 September 2021 / Published: 13 October 2021
(This article belongs to the Special Issue Multidisciplinary Composites)
Geopolymer is an eco-friendly material used in civil engineering works. For geopolymer concrete (GPC) preparation, waste fly ash (FA) and calcined clay (CC) together were used with percentage variation from 5, 10, and 15. In the mix design for geopolymers, there is no systematic methodology developed. In this study, the random forest regression method was used to forecast compressive strength and split tensile strength. The input content involved were caustic soda with 12 M, 14 M, and 16 M; sodium silicate; coarse aggregate passing 20 mm and 10 mm sieve; crushed stone dust; superplasticizer; curing temperature; curing time; added water; and retention time. The standard age of 28 days was used, and a total of 35 samples with a target-specified compressive strength of 30 MPa were prepared. In all, 20% of total data were trained, and 80% of data testing was performed. Efficacy in terms of mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and MSE (mean squared error) is suggested in the model. The results demonstrated that the RFR model is likely to predict GPC compressive strength (MAE = 1.85 MPa, MSE = 0.05 MPa, RMSE = 2.61 MPa, and R2 = 0.93) and split tensile strength (MAE = 0.20 MPa, MSE = 6.83 MPa, RMSE = 0.24 MPa, and R2 = 0.90) during training. View Full-Text
Keywords: fly ash; calcined clay; compressive strength; tensile strength; random forest regressor fly ash; calcined clay; compressive strength; tensile strength; random forest regressor
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MDPI and ACS Style

Gupta, P.; Gupta, N.; Saxena, K.K.; Goyal, S. Random Forest Modeling for Fly Ash-Calcined Clay Geopolymer Composite Strength Detection. J. Compos. Sci. 2021, 5, 271. https://doi.org/10.3390/jcs5100271

AMA Style

Gupta P, Gupta N, Saxena KK, Goyal S. Random Forest Modeling for Fly Ash-Calcined Clay Geopolymer Composite Strength Detection. Journal of Composites Science. 2021; 5(10):271. https://doi.org/10.3390/jcs5100271

Chicago/Turabian Style

Gupta, Priyanka, Nakul Gupta, Kuldeep K. Saxena, and Sudhir Goyal. 2021. "Random Forest Modeling for Fly Ash-Calcined Clay Geopolymer Composite Strength Detection" Journal of Composites Science 5, no. 10: 271. https://doi.org/10.3390/jcs5100271

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