The intelligent detection of distress in concrete is a research hotspot in structural health monitoring. In this study, Att-Unet, an improved attention-mechanism fully convolutional neural network model, was proposed to realize end-to-end pixel-level crack segmentation. Att-Unet consists of three parts: encoding module, decoding module, and AG (Attention Gate) module. The benefits associated with this module can effectively extract multi-scale features of cracks, focus on critical areas, and reconstruct semantics, to significantly improve the crack segmentation capability of the Att-Unet model. On the same data set, the mainstream semantic segmentation models (FCN and Unet) were trained simultaneously. Upon comparing and analyzing the calculated results of Att-Unet model with those of FCN and Unet, the results are as follows: for crack images under different conditions, Att-Unet achieved better results in accuracy, precision and F1-scores. Besides, Att-Unet showed higher feature extraction accuracy and better generalization ability in the crack segmentation task.

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To improve the properties of ground granulated blast furnace slag (GGBS) and utilize ground granulated blast furnace slag efficiently, this study investigates the effect of fineness on the hydration activity index (HAI) of ground granulated blast furnace slag. The hydration activity index of GGBS with six specific surface areas (SSAs) was characterized by the ratio of compressive strength of the prismatic mortar test block. The particle size distribution of GGBS with different grinding times was tested by laser particle size analyzer. The paste of different specific surface area GGBSs in different curing ages was investigated at the micro level by X-ray diffraction, scanning electron microscope, energy dispersive spectrometer, thermogravimetric scanning calorimeter, and differential scanning calorimeter. The effect of particle distribution of GGBS on the hydration activity index of different curing ages was studied by gray correlation analysis. The results indicated that the compressive strength and hydration activity index increases with the increase of a specific surface area of GGBS at different curing ages. The hydration activity index at different curing ages is almost a linear role for specific surface areas. With the increase in the specific surface area of GGBS, the content of Ca(OH)2 in paste decreases gradually. When GGBS was added into a mortar test block, the hydrate calcium silicate gel in paste changed from a high Ca/Si ratio to a low Ca/Si ratio. The 0–10 micron particles of GGBS particle distribution were highly correlated with the hydration activity index at different curing ages.