Estimating the Compressive Strength of Cement‐Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models

Hongxia Ma,Jia Zhang,Jiandong Liu,Jiandong Huang

doi:10.1155/2021/6629466

Abstract

To estimate the compressive strength of cement‐based materials with mining waste, the dataset based on a series of experimental studies was constructed. The support vector machine (SVM), decision tree (DT), and random forest (RF) models were developed and compared. The beetle antennae search (BAS) algorithm was employed to tune the hyperparameters of the developed machine learning models. The predictive performances of the three models were compared by the evaluation of the values of correlation coefficient (R) and root mean square error (RMSE). The results showed that the BAS algorithm can effectively tune these artificial intelligence models. The SVM model can obtain the minimum RMSE, while the BAS algorithm is inefficient in DT and RF models. The SVM, DT, and RF models can be used to predict the compressive strength of cement‐based materials using solid mining waste as aggregate effectively and accurately, with high R values and lower RMSE values. The RF algorithm can obtain the highest value of R and the lowest value of RMSE, demonstrating the highest accuracy. The solid mining waste to cement ratio is the most important variable to affect the compressive strength. Curing time was also an important parameter in the compressive strength of cemented materials, followed by the water‐solid ratio of mining waste and fine sand ratio.

Highlights

E support vector machine (SVM), decision tree (DT), and random forest (RF) models can be used to predict the compressive strength of cement-based materials using solid mining waste as aggregate effectively and accurately, with high R values and lower root mean square error (RMSE) values. e RF algorithm can obtain the highest value of R and the lowest value of RMSE, demonstrating the highest accuracy. e solid mining waste to cement ratio is the most important variable to affect the compressive strength
Based on a series of experimental studies on cement-based materials with mining waste as the aggregates, the dataset was constructed and the model was evaluated. ree machine learning models (SVM, DT, and RF) were used to predict the compressive strength of cement-based materials with mining waste as the aggregates, and the prediction results of different models were compared. e corresponding results are as follows: (1) e results of compressive strength of cement-based materials show that, with the increase of water content and curing time of solid mine waste, the compressive strength of cement-based materials increases, while it decreases with the increase of fine sand ratio and solid waste rock cement ratio
Due to the hyperparameters tuning of the beetle antennae search (BAS) algorithm, the SVM model can obtain the minimum RMSE, while the BAS algorithm is inefficient in DT and RF models

Summary

Introduction

Erefore, those solid mining wastes are a potential hazard to the air, water bodies, farmland, and villages [4, 13, 17,18,19,20] To address such issue of solid mining waste, researchers have started various approaches to recycling and reproduction of those construction materials using the mining waste, and one of the most representative cases is the widely used cement-based materials which can be produced environmentally friendly if the main aggregates can be replaced by the mining waste [21,22,23,24,25,26,27,28,29,30,31]. Limited studies can support accurate and efficient artificial intelligence techniques that target the compressive strength of the cemented-based materials that use the mining waste as the aggregates. It should be noted that the above machine learning techniques have been successfully adapted to the prediction of the concrete materials, but these studies still have the limitations of uncertainty, being time-consuming, and low efficiency

Methods

Results

Conclusion