scholarly journals Using Artificial Neural Network to Predict the Compressive Strength of Concrete containing Nano-silica

2013 ◽  
Vol 1 (3) ◽  
pp. 96-102
Author(s):  
Sakshi Gupta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Nano Silica ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

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

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Liyun Cui ◽  
Peiyuan Chen ◽  
Liang Wang ◽  
Jin Li ◽  
Hao Ling
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Gradient Boosting ◽  
Grey Relation Analysis ◽  
Extreme Gradient Boosting ◽  
Compressive Strength Of Concrete ◽  
Relation Analysis ◽  
Artificial Neural ◽  
Grey Relation

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.

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