Computers and Concrete

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Predicting the compressive strength of SCC containing nano silica using surrogate machine learning algorithms

  • Neeraj Kumar Shukla (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Aman Garg (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • Javed Bhutto (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Mona Aggarwal (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • Mohamed Abbas (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Hany S. Hussein (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Rajesh Verma (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • T.M. Yunus Khan (Mechanical Engineering Department, College of Engineering, King Khalid University)
  • Received : 2023.05.14
  • Accepted : 2023.06.05
  • Published : 2023.10.25
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Abstract

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.

Keywords

Acknowledgement

The authors gratefully acknowledge their respective organizations for their help and support. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University (KKU), Kingdom of Saudi Arabia for funding this work through the Small Group Research Project under Grant Number RGP1/70/44.

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