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Control method of spray curing system for cement concrete members based on the AdaBoost.M1 algorithm

Wei Yuan (School of Construction Machinery, Chang'an University, Xi'an, China)
Renfeng Yang (School of Construction Machinery, Chang'an University, Xi'an, China)
Jianyou Yu (Yanchong Temporary Preparatory Office of Hebei Province Expressway, Zhang Jiakou, China)
Qunrong Zeng (Fujian Road and Bridge Construction Co., Ltd, Fuzhou, China)
Zechen Yao (School of Construction Machinery, Chang'an University, Xi'an, China)

Construction Innovation

ISSN: 1471-4175

Article publication date: 13 December 2021

Issue publication date: 3 January 2023

26

Abstract

Purpose

Spray curing has become the preferred curing method for most cement concrete members because of its lower cost and sound effect. However, the spray curing quality of members is vulnerable to random variation environment factors and anthropogenic interferences. This paper aims to introduce the machine learning algorithm into the spray curing system to optimize its control method to improve the spray curing quality of members.

Design/methodology/approach

The critical parameters affecting the spray curing quality of members were collected through experiments, such as the temperature and humidity of the member's surface, the temperature, humidity and wind speed of the environment. The C4.5 algorithm was used as a weak classifier algorithm, and the AdaBoost.M1 algorithm was used to cascade multiple weak classifiers to form a robust classifier according to the collected data.

Findings

The results showed that the model constructed by the AdaBoost.M1 algorithm had achieved higher accuracy and robustness among the two algorithms. Based on the classification model built by the AdaBoost.M1 algorithm, the spray curing system can cause automatic decision-making spray switching according to the member's real-time curing state and environment.

Originality/value

With the classification model constructed by the AdaBoost.M1 algorithm, the spray curing system can overcome the disadvantages that external factors greatly influence the current control method of the spray curing system, and the intelligent control of the spray curing system was realized to a certain extent. This paper provides a reference for applying machine learning algorithms in the intellectual transformation of bridge construction equipment.

Keywords

Acknowledgements

The authors are grateful for the Science and Technology Research Project of Hebei Provincial Transport Department of China (No. YC-201922).

Citation

Yuan, W., Yang, R., Yu, J., Zeng, Q. and Yao, Z. (2023), "Control method of spray curing system for cement concrete members based on the AdaBoost.M1 algorithm", Construction Innovation, Vol. 23 No. 1, pp. 178-192. https://doi.org/10.1108/CI-07-2020-0124

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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