Abstract

In cold regions, hydraulic concrete structures are vulnerable to freeze–thaw (FT) damage, which is manifested by the evolution of microstructure in concrete. In order to reveal this evolution law, the rapid FT test is carried out on hydraulic concrete, and the damage evolution of hydraulic concrete under FT action is studied by using the combination of X-ray computed tomography (X-ray CT) technology and deep convolutional neural network (U-Net) image segmentation method. The results show that the U-Net image segmentation has higher accuracy than traditional threshold segmentation, and can capture microcracks and capillary pores caused by FT damage. The pores and cracks developed due to the FT cycles connect the isolated primary pores with each other, resulting in a significant increase in the connectivity and skeleton length of the internal pore structure of hydraulic concrete. In addition, the FT damage increases the number of pores and microcracks in interfacial transition zone (ITZ), and even degums aggregates from their surrounding mortar matrix, resulting in a continuous increase in the surface area of pores and microcracks in ITZ. Considering the FT damage of hydraulic concrete as a kind of fatigue effect, a FT damage evolution model based on continuous damage mechanics (CDM) is established, and the effectiveness of the model in predicting FT damage of hydraulic concrete is verified by comparing with the experimental results.

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