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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.

In response to China's rapid urbanization and the increasing accumulation of construction waste, it is essential to rationalize waste treatment without damaging the environment. Besides, natural mineral resources such as stone and sand are incredibly scarce. Therefore, it is urgent to find replaceable materials. The main objective of this study is to find a substitute for construction materials obtained by treating industrial tannery sludge harmlessly and effectively to evaluate its application in concrete. This paper explores the physical and mechanical properties of concrete mixed with sludge, and different pores were characterized by Nuclear Magnetic Resonance（NMR）. The result showed that replacing 4 % fine aggregate by sludge will not have significant adverse effect on the properties concrete under the condition that coarse aggregate is 100 % recycled and 10 % cement is replaced by sludge. Industrial sludge substitution for cementitious materials and fine aggregates can alleviate the problem of industrial sludge disposal to a certain extent, which is conducive to sustainable development.

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.