Author(s)

Wanli Xiong ¹, Yi Wang ¹, Junping Wu ¹, Zhichao Hu ¹, Bilian Li ¹

Affiliation(s)

¹ School of Computer Science and Engineering, Sichuan University of Science & Engineering, Zigong, Sichuan, 643000, China

Corresponding Author

Yi Wang

ABSTRACT

In this study, a hybrid optimization XGBoost model was used to predict the slump of concrete. This optimization model combines grid search and particle swarm optimization (PSO) algorithm. The grid search is used to determine the maximum depth and the number of trees in XGBoost, while the particle swarm optimization optimizes other floating-point hyperparameter ranges to improve the predictive accuracy of the model. The factors influencing the slump of concrete include water, cement, fine aggregate, coarse aggregate, and water reducer, which are represented by seven parameters. The model performs excellently in both the training and testing sets, with a coefficient of determination (R2) exceeding 0.97. In conclusion, this study demonstrates that the hybrid optimization of the XGBoost model using grid search and particle swarm optimization algorithm can accurately predict the slump of concrete, which is of significant importance for controlling and optimizing the concrete production process.

KEYWORDS

Concrete, slump, XGBoost algorithm, particle swarm, grid search

CITE THIS PAPER

Wanli Xiong, Yi Wang, Junping Wu, Zhichao Hu, Bilian Li, Concrete Slump Prediction Based on Hybrid Optimization XGBoost Algorithm. Advances in Computer, Signals and Systems (2023) Vol. 7: 74-83. DOI: http://dx.doi.org/10.23977/acss.2023.070610.

REFERENCES

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