Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

Jiandong Huang; Mengmeng Zhou; Hongwei Yuan; Mohanad Muayad Sabri Sabri; Xiang Li

doi:10.3390/ma15103500

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

Materials (Basel). 2022 May 13;15(10):3500. doi: 10.3390/ma15103500.

Authors

Jiandong Huang^{1

2}, Mengmeng Zhou¹, Hongwei Yuan¹, Mohanad Muayad Sabri Sabri², Xiang Li¹

Affiliations

¹ School of Mines, China University of Mining and Technology, Xuzhou 221116, China.
² Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia.

Abstract

Cement-based materials are widely used in construction engineering because of their excellent properties. With the continuous improvement of the functional requirements of building infrastructure, the performance requirements of cement-based materials are becoming higher and higher. As an important property of cement-based materials, compressive strength is of great significance to its research. In this study, a Random Forests (RF) and Firefly Algorithm (FA) hybrid machine learning model was proposed to predict the compressive strength of metakaolin cement-based materials. The database containing five input parameters (cement grade, water to binder ratio, cement-sand ratio, metakaolin to binder ratio, and superplasticizer) based on 361 samples was employed for the prediction. In this model, FA was used to optimize the hyperparameters, and RF was used to predict the compressive strength of metakaolin cement-based materials. The reliability of the hybrid model was verified by comparing the predicted and actual values of the dataset. The importance of five variables was also evaluated, and the results showed the cement grade has the greatest influence on the compressive strength of metakaolin cement-based materials, followed by the water-binder ratio.

Keywords: compressive strength; firefly algorithm; hyperparameters; machine learning; random forests.

Grants and funding

The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program “Priority 2030” (Agreement 075-15-2021-1333 dated 30 September 2021).