Fly ash, granulated blast furnace slag, marble waste powder, etc. are just some of the by-products of other sectors that
the construction industry is looking to include into the many types of concrete they produce. This research seeks to use surrogate
machine learning methods to forecast the compressive strength of self-compacting concrete. The surrogate models were
developed using Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Process Regression (GPR) techniques. Compressive strength is used as the output variable, with nano silica content, cement content, coarse aggregate content, fine aggregate content, superplasticizer, curing duration, and water-binder ratio as input variables. Of the four models, GBM had the highest accuracy in determining the compressive strength of SCC. The concrete's compressive strength is worst predicted by GPR. Compressive strength of SCC with nano silica is found to be most affected by curing time and least by fine aggregate.
Neeraj Kumar Shukla, Mohamed Abbas, Hany S. Hussein and Rajesh Verma: Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Kingdom of Saudi Arabia
Aman Garg and Mona Aggarwal: Department of Multidisciplinary Engineering, The NorthCap University, Gurugram, Haryana, India - 122017
T.M. Yunus Khan: Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Kingdom of Saudi Arabia
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