The aim of this article is to reduce the environmental harm caused by industrial solid waste, specifically tannery sludge, and enable its reutilization. This study prepared an alkali-activated tannery sludge–slag solidification product (ATSSP) with high mechanical properties using blast furnace slag and tannery sludge as raw materials. The response surface methodology (RSM) was used to optimize the product mix ratio. The hydration mechanism and solidification method of ATSSP for Cr in tannery sludge were studied using X-ray diffraction (XRD), scanning electron microscope-energy dispersive spectrometer (SEM-EDS), and Fourier transform infrared reflection (FT-IR). The results indicate that the compressive strength regression model established through RSM has high accuracy and credibility. When the ratio of activator to binder is 0.2174, the alkali activation modulus is 1.02, and the water-to-cement ratio is 0.37; the 28 d compressive strength of ATSSP can reach 71.3 MPa. The sulfate in tannery sludge can promote the secondary hydration reaction of slag, and the generated hydrated calcium silicate and calcite greatly improve the strength of the ATSSP. The reducing substances contained in slag can reduce Cr (VI) in tannery sludge to Cr (III) in the form of uvarovite. The total Cr and Cr (VI) precipitation concentrations of the product are far less than the specification requirements.

Seepage analysis is always a concern in dam safety and stability research. The prediction and analysis of seepage pressure monitoring data is an effective way to ensure the safety and stability of dam seepage. With the timeliness of a change in a monitoring value and lag due to external influences, a RS‐LSTM model written in Python is developed in this paper which combines rough set theory (RS) and the long‐ and short‐term memory network model (LSTM). The model proposed calculates the prediction score of the seepage pressure of a dam experiencing multiple effects by preordering factor importance values to eliminate the interference of redundant factors. A case study shows that the water level, rainfall, temperature, and duration are all factors that affect the seepage pressure, and their importance values decrease successively. Thus, the seepage pressure of a dam can be predicted with a determination coefficient R² of 0.96. Compared with the recurrent neural network (RNN) model and BP neural network model, the training time of the RS‐LSTM model proposed is 6.37 s, and the operation efficiency is 41% and 59% higher than that of the RNN and BP models, respectively. The mean relative error is also 3.00%, which is 50% lower than that of the RNN model and 31% lower than that of the BP model. Based on these results, this model has the advantages of fast computation speed and high accuracy in prediction.

To address the challenges of performing in-situ tests on riverbed overburden gravel, this study employs three scaling methods—equal mass substitution, similar gradation, and the mixed method—to investigate the original gradation of the gravel. Large-scale triaxial consolidated drained shear tests were conducted to evaluate the effects of the maximum particle size reduction ratio (M) and confining pressure on the stress–strain behavior, fractal dimension, particle breakage, and the parameters of the Duncan-Chang model (an elastic model describing nonlinear stress-strain relationships). The study explores how scaling, based on fractal dimension and particle breakage rate, impacts the strength and deformation characteristics of gravel materials. The results show that increasing confining pressure typically leads to higher material strength. As confining pressure increases, particle breakage becomes more pronounced across the various scaling methods. Gravel processed using the similar gradation method exhibited the highest strength, the smallest change in fractal dimension, and the lowest breakage rate, followed by the mixed method. Furthermore, as the M value increases, peak stress also rises, with the modulus coefficient (k) and bulk modulus coefficient (K_b) in the Duncan-Chang model increasing by 4.5 times and 3.3 times, respectively, for M = 15 compared to M = 0. A clear relationship was observed between the change in fractal dimension before and after testing and the breakage index (B_g). As the M value increases, both the difference in fractal dimension and the breakage rate decrease, suggesting that larger M values lead to reduced particle breakage and improved mechanical stability.

The study of freeze-thaw (F-T) damage of steam cured concrete (SCC) plays a positive role in promoting the development of prefabricated technology in water conservancy project construction and construction industrialization in cold areas. This study aims to investigate and provide a better understanding of the surface damage pattern and pore structure of SCC. In this paper, a 3D scanner was used to scan the surface topography of SCC under five conditions of F-T (the number of F-T was 0, 50, 100, 150, and 200 respectively), and the spatial distribution of point cloud data generated by scanning results was analyzed, and surface roughness was introduced to quantify the damage effect of F-T cycle on the surface of SCC. The results show that the fluctuation range of point cloud data increases from 0–0.15 mm to 0–0.93 mm with the increase of F-T cycles. The F-T cycles result in the continuous deterioration of the surface of SCC, and surface roughness increases from 0.097 mm to 0.899 mm, and the development law basically conforms to the exponential growth law. In addition, the pore structure parameters of SCC under different F-T cycles were obtained based on X-ray computed tomography technology (XCT). It was found that the pore size and porosity increased with the increase of F-T cycles, and the growth rates ranged from 3.41% to 19.69% and 29.29% to 41.85%, respectively. The pore numbers showed a decreasing trend, with the decreasing rate ranging from 2.56% to 11.56%. It is also found that the inhomogeneity of pore space distribution caused by F-T cycles may be one of the main reasons affecting the mechanical properties of SCC. On this basis, the surface fractal dimension and volume fractal dimension of SCC pores were calculated respectively, and they were used to evaluate the F-T damage degree of SCC under different F-T cycles. The results revealed that there is a linear relationship between fractal dimension and F-T cycles. When the fractal dimension is used to characterize the F-T damage degree of SCC, the result of the volume fractal dimension is better than the surface fractal dimension.