Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete

Abstract

Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R2) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R2 value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter’s contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers.

Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters

“…In comparison, Wang et al [84] also anticipated the CS of geopolymer concrete by using the AdaBoost, random forest, and decision tree algorithms and reported the R 2 value equal to 0.90, 0.90, and 0.83, respectively. Cao et al [86] also employed SVM and MLP approaches for the CS of geopolymer concrete and reported the R 2 result as 0.91 and 0.88, respectively. It also indicates that the selected algorithms in the present study performed better than the approaches used in the previous studies.…”

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

“…[63][64][65][66][67][68][69][70]. Estimations of various characteristics of conventional and advanced concretes, such as durability, thermal characteristics, and mechanical characteristics, have been extensively covered in previous studies [71][72][73][74].…”

Compressive Strength Prediction of Fly Ash Geopolymer Concrete Using Support Vector and Random Forest Regression

“…Some other researchers developed compressive strength predictive models of fly ash based geopolymer concrete (FGPC) using Support Vector Machine (or) Regression (SVM/ SVR) in addition to Random Forest Method, Back propagation neural network, Multilayer perceptron and Extreme gradient boosting algorithm etc. It was concluded that all these models can be effectively employed in the prediction of compressive strength of FGPC [34][35][36].…”