Physical simulation is one of the effective methods to study mining problems, but the selection and proportion of simulation materials are greatly affected by the regional environment. This paper is based on a multilevel orthogonal design test scheme using sand, lime, and gypsum as the materials in the Shangwan coal mine in the Shendong coalfield, with the sand to cement ratio, paste to ash ratio, and maintenance days as variables. The effect of the polar difference method on the strength and density of gypsum was used as a reference for physical simulation in the Shendong coalfield. The sensitivity analysis of each factor was carried out by the polar difference method, and the influencing factors on density were, in descending order, sand to mortar ratio, mortar to ash ratio, and the number of maintenance days; the influencing factors on strength were, in descending order, mortar to ash ratio, maintenance days, and sand to mortar ratio. The sand cement ratio was negatively correlated with strength and density, the paste to ash ratio was positively correlated with strength and density, and the number of maintenance days was positively correlated with strength and negatively correlated with density. The multivariate non-linear regression analysis of sand to cement ratio and paste to ash ratio identified similar material proportioning test equations for the Shendong coalfield, which can improve the accuracy of physical simulation and be used to guide physical simulation experiments in the Shendong coalfield.

Soil moisture is key in maintaining surface energy balance and the hydrological cycle. However, the patterns of soil moisture change at different altitudes and their response to rainfall are not fully understood. The aim of this study is to analyze the changes in soil moisture at different altitudes in the Mu Us Sandy Land, assess the soil moisture response to various rainfall events, and evaluate the factors affecting this response. To achieve this, we set up two soil moisture and precipitation monitoring systems in the Ejin Horo Banner, located at the northern edge of the Mu Us Sandy Land. Results show that the annual precipitation at the site was 342.6 mm, with most rain falling between July and September. The region experiences soil drought from June to October. At the lower X1 test site, the increase in soil moisture in the 0–90 cm layer during rainfall was greater than the amount of precipitation, suggesting that lateral water flow from higher areas influences soil moisture at lower positions. This study used Grey Relational Analysis to identify the key factors influencing soil moisture changes; the result showed that the initial soil moisture and total precipitation have the strongest correlation with changes in soil moisture, making them the main factors controlling soil moisture response at the site. These findings enhance our understanding of soil moisture dynamics and provide a foundation for vegetation restoration in semi-arid sandy areas and ecosystem rehabilitation.

Revealing the water use pattern of plants influenced by coal-mining-caused land subsidence is crucial to understand plant–water interactions and guide ecological restoration. However, available information on herbaceous plants, the dominant species in most arid and semi-arid regions with abundant coal resources, remains inadequate. We investigated the water use patterns of Stipa bungeana Trin. by measuring soil water content, root distribution, and stable isotopes of hydrogen (δ²H) and oxygen (δ¹⁸O) of soil water and plant stem water both before and after a rainfall event. The results revealed that prior to rainfall, both areas exhibited a low soil water content with no discernible difference in soil drought. However, the soil waters δ²H and δ¹⁸O were found to be more enriched at varying depths within the subsidence area, indicating a heightened level of soil evaporation. Both soil water content and soil water isotopic composition responded sensitively to rainfall, with rainfall primarily replenishing the shallow layer (0–20 cm), thereby reflecting an infiltration mode dominated by piston flow. More water seeped into deeper soil layers in the subsidence area compared to the non-subsidence area, with more preferential flow. Before rainfall, the sources of plant water uptake were consistent both at shallow and deep soil layers, implying that the proportion of water uptake gradually decreased with increasing depth. After rainfall, the sources of plant water uptake differed slightly between the two soil layers. The plants in non-subsidence and subsidence areas dominantly extracted soil water at depths greater than 10 cm and 20 cm, respectively. The root system in the subsidence area was more developed than that in the non-subsidence area. Plant water uptake was primarily influenced by the spatial distribution of roots, as well as the post-rainfall water distribution, regardless of whether they were in the subsidence area or not. Although land subsidence affected soil water transport, the water uptake pattern of Stipa bungeana Trin. was similar before and after rainfall, indicating the adaptive growth of plants through their roots in the subsidence area. The high adaptability of herbs such as Stipa bungeana Trin. makes them a viable option for vegetation restoration in subsidence areas. This study has significant implications for evaluating plant–water relationships in subsidence areas due to coal mining, thereby providing a fundamental basis and valuable reference for ecological restoration and management strategies within such affected regions.

Rapid coal mining results in a series of mining subsidence damages. Predicting surface movement and deformation accurately is essential to reducing mining damage. The accurate determination of parameters for a mining subsidence prediction model is crucial for accurately predicting mining subsidence. In this research, with the incorporation of the Sobol sequence and Lévy flight strategy, we propose an improved whale optimization algorithm (IWOA), thereby enhancing its global optimization capability and mitigating local optimization issues. Our simulation experiment results demonstrate that the IWOA achieved a root mean square error and relative error of less than 0.42 and 0.27%, respectively, indicating its superior accuracy compared to a basic algorithm. The IWOA inversion model also exhibits superior performance compared to a basic algorithm in mitigating gross error interference, Gaussian noise interference, and missing observation point interference. Additionally, it demonstrates enhanced global search capabilities. The IWOA was employed to perform parameter inversion for the working face 1414(1) in Guqiao Coal Mine. The root mean square error of the inversion results did not exceed 6.03, while the subsidence coefficient q, tangent of the main influence angle tanβ, horizontal movement coefficient b, and mining influence propagation angle θ were all below 0.32. The average value of the fitted root mean square error for the subsidence value’s fitted root mean square error and horizontal movement value’s fitted root mean square error of the IWOA was 91.51 mm, which satisfies the accuracy requirements for general engineering applications.