A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions

Abstract

Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667.

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

“…Furthermore, K-fold cross-validation can give a more accurate evaluation of the models' capacity for prediction while reducing bias and variance issues brought on by incorrectly partitioned datasets. To address the issues of overfitting and underfitting in the training dataset, this work divides the dataset into ten folds [31].…”

“…MSE and MAE can measure the predictive ability of the model by calculating the difference between the true and predicted values. MSE, MAE, and R are commonly used indicators to measure the predictive ability of regression models [17,31]. Equations ( 2)-( 4) can be used to calculate MSE, R, and MAE, respectively.…”

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

“…However, the production of cement requires a large amount of energy and produces a large amount of carbon dioxide during the preparation process. Geopolymer concrete uses geopolymer materials as the main gelling agent [1][2][3][4]. Compared with traditional cement concrete, its material and preparation process have changed.…”