Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature

Abstract

High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

“…This investigation is concerned with the influence of input parameters on forecasting the CS of SCM-based concrete. The input parameters have a considerable impact on the outcome projection [ 46 ]. The influence of each input parameter on the CS prediction of concrete is depicted in Figure 11 .…”

“…This also supports the higher accuracy of the bagging technique for the prediction of the results. The literature also indicated that bagging models produce more accurate outcomes than other ML approaches [ 29 , 46 ]. Moreover, a sensitivity analysis was performed to determine the impact of each input parameter on the prediction of the CS of SCM-based concrete.…”

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

“…In general, two methods of ML are used for modeling and predicting. Firstly, there is the traditional solution built on a single in-dependent paradigm, while secondly, there are collective learning algorithms, including boosting, bagging, and random forests created on many components of the data-base [ 55 ]. Individual ML models have weak learners who tend to produce overfitting of the data.…”

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

“…The purpose of this evaluation is to determine the impact of input variables on forecasting the C-S of HPC. The input parameters have a considerable influence on the projected outcome [24]. Figure 12 illustrates the effect of each input parameter on the C-S prediction of HPC.…”

“…In recent years, machine learning (ML) algorithms have demonstrated significant potential for forecasting cementitious material properties [22][23][24][25][26][27][28]. Among the numerous machine learning methods, support vector regression (SVR) and artificial neural network (ANN) methods have been widely utilized to predict concrete parameters such as compressive strength (C-S) [29], split-tensile strength, elastic modulus, and so on [30][31][32].…”