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.
Highlights
A series of dilemmas including waste emission and overconsumption of energy and natural resources have been currently pressing worldwide concerns because of global population explosion and rapid urbanization
Combining with machine learning, some researchers utilize various basic prediction models, such as logistic regression (LR), random forest model (BRF), support vector machine (SVM), and artificial neural network (ANN) [4,5,6]. ese models are commonly used for predicting compressive strength of concrete, irrespective of costly and time-consuming nature. e artificial neural network, inspired by biological systems of the human brain, can learn and generalize from experience without prior knowledge
The proposed novel predictive system XGBoost was successfully applied to predict the compressive strength of concrete containing slag and metakaolin, and its performance was compared with the commonly used ANN and optimized genetic algorithm-artificial neural network (GA-ANN) models. e datasets used for the training and testing three models were selected from 18 research articles, with the treatments of unifying the compressive strength of concrete, normalization of data, and selection of main influencing factors
Summary
A series of dilemmas including waste emission and overconsumption of energy and natural resources have been currently pressing worldwide concerns because of global population explosion and rapid urbanization. Erefore, it is beneficial to find and utilize active admixtures with highquality and low-energy consumption as alternatives of cement, partly or totally [1, 2] Those active admixtures can even enhance the properties of concrete such as compressive strength, antipermeability, and corrosion resistance [3]. In KDDCup competition including commercial sales forecast, the team of Top 10 used the XGBoost algorithm for web page text classification, customer behavior prediction, ad click rate prediction, and hazard risk prediction and other fields [14] In consideration of those outstanding achievements, in this study, XGBoost was applied for predicting the compressive strength of concrete containing slag and metakaolin. To train and test those prediction models, 600 groups of data selected from 18 research papers (Table 1) were utilized, after a necessary pretreatment process, i.e., unifying the compressive strength of concrete under different dimensions to avoid the influence of dimension effect. Ose data were divided into training dataset and testing dataset. e quantitative analysis of prediction performance of ANN, GA-ANN, and XGBoost was obtained by testing the value of absolute fraction of variance (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE)
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