Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions

Abstract

Incorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and it is necessary to evaluate the optimal performance. Accordingly, artificial intelligence has been used recently to evaluate the performance of concrete compressive behaviour for different types of construction material. Therefore, supervised machine learning techniques, i.e., DT-XG Boost, DT-Gradient Boosting, SVM-Bagging, and SVM-Adaboost, are executed in the current study to predict RCAC’s compressive strength. Additionally, SHapley Additive exPlanations (SHAP) analysis shows the influence of input parameters on the compressive strength of RCAC and the interactions between them. The correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the model’s performance. Subsequently, the k-fold cross-validation method is executed to validate the model’s performance. The R2 value of 0.98 from DT-Gradient Boosting supersedes those of the other methods, i.e., DT- XG Boost, SVM-Bagging, and SVM-Adaboost. The DT-Gradient Boosting model, with a higher R2 value and lower error (i.e., MAE, RMSE) values, had a better performance than the other ensemble techniques. The application of machine learning techniques for the prediction of concrete properties would consume fewer resources and take less time and effort for scholars in the respective engineering field. The forecasting of the proposed DT-Gradient Boosting models is in close agreement with the actual experimental results, as indicated by the assessment output showing the improved estimation of RCAC’s compressive strength.

Machine learning models to predict the relationship between printing parameters and tensile strength of 3D Poly (lactic acid) scaffolds for tissue engineering applications

“…A correlation heatmap was produced by Seaborn module of Phyton to determine the strength of relationship between the independent variables (nozzle temperature, printing speed, layer height and infill density) and the dependent variable (tensile strength) [25,33]. A higher correlation coefficient (r) indicates multicollinearity between independent variables [18].…”

Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete

“…In current data, the correlation was weak (i.e., less than 0.5) for most of the input parameters; therefore, in this situation, there would be no multicollinearity issues as the result of microscopic differences. In the case of stronger correlation (i.e., near to 1), the multicollinearity issues would be higher, and the input variables would have a significantly higher impact that ultimately would influence the outcomes and may offer less precise findings (Amin et al, 2022;Pan et al, 2022).…”

Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures

“…This study employs the k-fold approach to validate the implemented algorithm. In the literature (Amin et al, 2022a;Khan et al, 2022a;2022b;Zou et al, 2022), the statistical analysis is reported to assess the model's performance. Usually, data splitting into ten subgroups is carried out for the random dispersion to perform the k-fold process for cross-validation; this approach is repeated ten times to achieve outcomes in a satisfactory range, as shown in Figure 17.…”