Predicting the Compressive Strength of the Cement-Fly Ash-Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method

Jiandong Huang; Mohanad Muayad Sabri Sabri; Dmitrii Vladimirovich Ulrikh; Mahmood Ahmad; Kifayah Abood Mohammed Alsaffar

doi:10.3390/ma15124193

Predicting the Compressive Strength of the Cement-Fly Ash-Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method

Materials (Basel). 2022 Jun 13;15(12):4193. doi: 10.3390/ma15124193.

Authors

Jiandong Huang^{1

2}, Mohanad Muayad Sabri Sabri², Dmitrii Vladimirovich Ulrikh³, Mahmood Ahmad⁴, Kifayah Abood Mohammed Alsaffar⁵

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.
³ Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia.
⁴ Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan.
⁵ University of Mashreq, Baghdad 10023, Iraq.

Abstract

Concrete is the most widely used material in construction. It has the characteristics of strong plasticity, good economy, high safety, and good durability. As a kind of structural material, concrete must have sufficient strength to resist various loads. At the same time, due to the brittleness of concrete, compressive strength is the most important mechanical property of concrete. To solve the disadvantages of the low efficiency of the traditional concrete compressive strength prediction methods, this study proposes a firefly algorithm (FA) and random forest (RF) hybrid machine-learning method to predict the compressive strength of concrete. First, a database is built based on the data of published articles. The dataset in the database contains eight input variables (cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age) and one output variable (concrete compressive strength). Then, the correlation of the eight input variables was analyzed, and the results showed that there was no high correlation between the input variables; thus, they could be used as input variables to predict the compressive strength of concrete. Next, this study used the FA algorithm to optimize the hyperparameters of RF to obtain better hyperparameters. Finally, we verified that the FA and RF hybrid machine-learning model proposed in this study can predict the compressive strength of concrete with high accuracy by analyzing the R values and RSME values of the training set and test set and comparing the predicted value and actual value of the training set and test machine.

Keywords: compressive strength; concrete; hybrid machine-learning method.

Grants and funding

The research was 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).