Affiliations: [a] School of Civil Engineering, Lanzhou Jiaotong University, Lanzhou, China
| [b] National and Provincial Joint Engineering Laboratory of Road & Bridge Disaster Prevention and Control, Lanzhou Jiaotong University, Lanzhou, China
Correspondence:
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Corresponding author. Qicai Wang, School of Civil Engineering, Lanzhou Jiaotong University, Lanzhou, China. E-mail: 18093194521@163.com.
Abstract: The compressive strength of concrete can be predicted by machine learning. One thousand thirty samples of concrete compressive strength data were used as the dataset. Machine learning was applied to prediction of concrete compressive strength with seven machine learning algorithms. To improve data utilization and generalization ability of machine learning model, ten data sets were constructed by feature reorganization for data augmentation. Compared with other machine learning models, the XGBoost model based on Boosting tree algorithm had the highest prediction accuracy and the most robust generalization ability. With different multi-feature combination input conditions, the R2 score of the XGBoost algorithm was 0.9283, the MAE score was 3.4292, the MAPE score was 12.5656, and the RMSE score was 5.2813. The error accumulation curve of the XGBoost algorithm was analyzed. When the compressive strength of concrete is at 5–20MPa, the error contribution rate is higher. When the concrete compressive strength is at 20–40MPa, the prediction result error of the model drops sharply. When the strength reaches 40MPa, the error contribution rate of the model tends to converge and the error contribution rate is stable between 1 and 1.2, which indicates that the model has high prediction accuracy when the compressive strength is higher than 40 MPa.
Keywords: Machine learning, prediction of Compressive strength, feature reorganization, XGBoost, data enhancement
