Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete

Abstract

The prediction of concrete strength is an interesting point of investigation and could be realized well, especially for the concrete with the complex system, with the development of machine learning and artificial intelligence. Therefore, an excellent algorithm should put emphasis to receiving increased attention from researchers. This study presents a novel predictive system as follows: extreme gradient boosting (XGBoost) based on grey relation analysis (GRA) for predicting the compressive strength of concrete containing slag and metakaolin. One of its highlights is a feature selection methodology, i.e., GRA, which was used to determine the main input variables. Another highlight is that its performance was compared with the frequently used artificial neural network (ANN) and genetic algorithm-artificial neural network (GA-ANN) by using random dataset and the same testing datasets. For three same testing datasets, the average R2 values of ANN, GA-ANN, and XGBoost are 0.674, 0.829, and 0.880, respectively, indicating that XGBoost has the highest absolute fraction of variance (R2). XGBoost can provide best result by testing the root mean squared error (RMSE) and mean absolute percentage error (MAPE). The average RMSE values of ANN, GA-ANN, and XGBoost are 15.569 MPa, 10.530 MPa, and 9.532 MPa, respectively, and those of MAPE of ANN, GA-ANN, and XGBoost are 11.224%, 9.140%, and 8.718%, respectively. Thus, the XGBoost definitely performed better than the ANN and GA-ANN. Finally, a type of application software based on XGBoost was developed for practical applications. This vivid software interfaces could help users in prediction and easy and efficient analysis.

Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization

“…The MAE measures the average magnitude of the errors (the difference between observed and predicted values), regardless of their direction. It can be determined using (15) [45]. In MAE, large errors caused by outliers are not so important, because this metric is absolute and not quadratic [44].…”

“…Likewise, Mustapha and Mohamed [14], also using Yeh's [13] dataset without cross-validation, obtained an R 2 of 0.93 by applying the Support Vector Regression (SVR). Finally, Cui et al [15] used a decision tree model for this same purpose, obtained an R 2 above 0.80, and concluded that these models are suitable to assist in the mix design of concretes.…”

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

“…GB is capable of both classification and regression tasks. GB had been recently used in various fields including rain prediction [4], rainfall prediction [12], transpiration estimation [13], undrained shear strength prediction [14], concrete strength prediction [15], groundwater level prediction [16], and solar irradiation forecasting [17].…”

Investigation on the Mechanical Properties and Strengthening Mechanism of Solid-Waste–Sulfur-Based Cementitious Composites

“…Zhu et al [ 21 ] established a forecast model of the CS of concrete with recycled aggregate according to the gray correlation analysis. Cui et al [ 22 ] also predicted the CS of concrete containing slag and metakaolin by the extreme gradient enhancement method based on the gray correlation evaluation. Mokhtar et al [ 23 ] and Jin et al [ 24 ] considered that the employment of the gray relational principle to discuss the affecting factors and changing rules of concrete strength is feasible.…”