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This article aims to improve the toughness of pre-packaged grouts (PPG) by incorporating crumb rubber. The mechanism for toughness of PPG with crumb rubber was analyzed based on the uniaxial compression model. Crumb rubber with surfaces treated by different methods (NaOH solutions or microwave treatment) was observed by scanning electron microscopy (SEM). The effects of mesh sizes, amounts, surface-treated methods of crumb rubber, and mixing procedures on the PPG’s mechanical strength and rheological properties were investigated. The results showed that, firstly, the addition of crumb rubber improves the PPG’s toughness, while its mechanical strength is reduced. Adding NaOH solutions or microwave-treated crumb rubber into PPG can weaken the negative effects of crumb rubber on the PPG’s mechanical strength; however, this function is limited. Secondly, the crumb rubber grouts’ rheological properties can be fully exploited by increasing the stirring rate and time so that the fluidity of crumb rubber grouts is improved, which fulfils the characteristics of no bleeding and micro-expansion. Finally, the optimal formula and mixing technique of crumb rubber grouts were proposed in this paper.The results of this paper can provide a significant reference for the application of scrap tires.

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.