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Computers and Concrete Volume 32, Number 4, October 2023 , pages 373-381 DOI: https://doi.org/10.12989/cac.2023.32.4.373 |
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Predicting the compressive strength of SCC containing nano silica using surrogate machine learning algorithms |
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Neeraj Kumar Shukla, Aman Garg, Javed Bhutto, Mona Aggarwal, Mohamed Abbas, Hany S. Hussein, Rajesh Verma and T.M. Yunus Khan
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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. | ||
| Key Words | ||
| compressive strength; IoT; self-compacting concrete; sensitivity; surrogate machine learning | ||
| Address | ||
| 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 | ||