Differential evolution–based integrated model for predicting concrete slumps

Abstract

Concrete slump, a crucial indicator of fluidity, directly affects the pumpability and construction efficiency of concrete. Conventionally, concrete slumps are assessed through multiple test iterations conducted by skilled professionals to obtain accurate results. In this study, a predictive model was established to predict concrete slumps directly based on concrete mix proportions, thereby mitigating labor and time costs. An open-source dataset was used in this study. The selected water-cement ratio ranged from 0.29 to 0.66. Cement, slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate were employed as input parameters. Furthermore, the performances of a variety of algorithms in predicting concrete slumps were analyzed and compared. The algorithms included SVM, KNN, Extra-Trees, Gradient Boosting, Decision Tree, Elastic Net, Lasso, Ridge, Random Forest, Bagging, AdaBoost, and XGBoost. Through a comprehensive comparison of the prediction performance of these models, AdaBoost, Bagging, Extra-Trees, and Random Forest were adopted as base models. Moreover, the SVM algorithm optimized using Differential Evolution was employed as a secondary model to construct an enhanced integrated prediction model. The final prediction model boasts an MSE of 2.099984, MAE of 1.225597, RMSE of 1.449132, and R2 of 0.970418. Compared to conventional prediction models, the proposed model has the potential to substantially improve prediction performance.

Keywords

• Concrete slump
• Fly ash
• Machine learning
• Model prediction
• Data Mining
• Comparison