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Computers and Concrete Volume 30, Number 1, July 2022 , pages 33-42 DOI: https://doi.org/10.12989/cac.2022.30.1.033 |
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Machine learning models for predicting the compressive strength of concrete containing nano silica |
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Aman Garg, Paratibha Aggarwal, Yogesh Aggarwal, M.O. Belarbi, H.D. Chalak, Abdelouahed Tounsi and Reeta Gulia
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| Abstract | ||
| Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a time-consuming and laborious process. The present study aims to propose surrogate models based on Support Vector Machine (SVM) and Gaussian Process Regression (GPR) machine learning techniques, which can predict the CS of concrete containing nano-silica. Content of cement, aggregates, nano-silica and its fineness, water-binder ratio, and the days at which strength has to be predicted are the input variables. The efficiency of the models is compared in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE), Variance Account For (VAF), Nash-Sutcliffe Efficiency (NSE), and RMSE to observation's standard deviation ratio (RSR). It has been observed that the SVM outperforms GPR in predicting the CS of the concrete containing nano-silica. | ||
| Key Words | ||
| compressive strength; concrete; GPR; machine learning; nano-silica; SVM | ||
| Address | ||
| Aman Garg: Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Uttar Pradesh, 208016, India; Department of Civil and Environmental Engineering, The NorthCap University, Gurugram, Haryana, 122017, India Paratibha Aggarwal, Yogesh Aggarwal: Department of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, 136119, India M.O. Belarbi: Laboratoire de Recherche en Génie Civil, LRGC. Université de Biskra B.P. 145, R.P. 07000, Biskra, Algeria H.D. Chalak: Department of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, 136119, India Abdelouahed Tounsi: YFL (Yonsei Frontier Lab), Yonsei University, Seoul, Korea; Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Eastern Province, Saudi Arabia; Civil Engineering Department, Faculty of Technology, Material and Hydrology Laboratory, University of Sidi Bel Abbes, Algeria Reeta Gulia: Department of Civil Engineering, DPG Institute of Technology and Management, Gurugram, Haryana, 122004, India | ||