In this paper, a novel method for non-destructive detection of steelcorrosion in bridge reinforced concrete is proposed with inductionheating and infrared thermography.

Concrete structures are vulnerable to sulfate attacks during their service life, as sulfate ions react with cement hydration products to form expansive phases, generating internal stresses that cause mechanical degradation. In this study, a cementitious capillary crystalline waterproofing material (CCCW) was incorporated into concrete to mitigate sulfate ingress and enhance sulfate resistance. The evolution of compressive strength, ultrasonic pulse velocity, dynamic elastic modulus, and the microstructure of concrete was investigated in sulfate-exposed concretes with varying CCCW dosages and strength grades; the sulfate ion concentration profiles were also analyzed. The results indicate that the enhancement effect of CCCW on sulfate resistance declines progressively with increasing concrete strength. The formation of calcium silicate hydrate and calcium carbonate fills the pores of concrete, hindering the intrusion of sulfate solution. Moreover, the self-healing effect of concrete further inhibits the diffusion of sulfate ions through cracks, improving the sulfate resistance of concrete. These findings provide critical insights and practical guidance for improving concrete resistance to sulfate-induced deterioration.

This paper addresses the issue of temperature stress in large-volume concrete during the pouring process caused by the heat of hydration, proposing a computational model based on the finite element method. By analyzing the experimental and empirical data on the material properties of concrete, a cubic concrete model was developed using ANSYS software to investigate the distribution of temperature and stress fields, revealing that peak temperature stress is concentrated at the top of the third layer. Furthermore, a database correlating concrete mix ratios and peak temperature stress was constructed, and ensemble learning methods were employed to train and test predictive models, identifying XGBoost as the optimal model. Additionally, the SHAP method was applied to interpret the model, revealing the influence mechanisms of factors such as cement and blast furnace slag on temperature stress, and proposing mix ratio adjustments based on SHAP values to reduce temperature stress. The findings provide theoretical and practical guidance for temperature control design and structural optimization of large-volume concrete.