Department of Mining Engineering, University of Kashan, Kashan, Iran.
Environ Sci Pollut Res Int. 2024 May;31(22):31942-31966. doi: 10.1007/s11356-024-33300-2. Epub 2024 Apr 19.
Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertical displacement of the ground, known as subsidence. This subsidence can extend to the surface as trough subsidence, as the movement and deformation of the hanging-wall rocks of the mining stope propagate upwards. Accurately predicting subsidence is crucial for estimating damage and protecting surface buildings and structures in mining areas. Therefore, developing a model that considers all relevant parameters for subsidence estimation is essential. In this article, we discuss the prediction of land subsidence caused by the caving of a stop's roof, focusing on coal mining using the longwall method. The main aim of this research is to improve the accuracy of prediction models of land subsidence due to mining. For this purpose, we consider a total of 11 parameters related to coal mining, including mining thickness and depth (related to the deposit), as well as density, cohesion, internal friction angle, elasticity modulus, bulk modulus, shear modulus, Poisson's ratio, uniaxial compressive strength, and tensile strength (related to the overburden). We utilize information collected from 14 coal mines regarding mining and subsidence to achieve this. We then explore the prediction of subsidence caused by mining using the gene expression programming (GEP) algorithm, optimized through a combination of the artificial bee colony (ABC) and ant lion optimizer (ALO) algorithms. Modeling results demonstrate that combining the GEP algorithm with optimization based on the ABC algorithm yields the best subsidence prediction, achieving a correlation coefficient of 0.96. Furthermore, sensitivity analysis reveals that mining depth and density have the greatest and least effects, respectively, on land surface subsidence resulting from coal mining using the longwall method.
地面沉降是一种环境危害，是由于地下资源的开采而引起的。在地下采矿中，当矿物质材料被从地下深处开采出来时，采矿空间的掏空或崩塌会导致地面的垂直位移，即沉降。这种沉降会延伸到地表，形成槽形沉降，因为采矿硐室的悬挂岩石的移动和变形向上传播。准确预测沉降对于估算矿区内的破坏和保护地表建筑物和结构至关重要。因此，开发一个考虑到沉降估计所有相关参数的模型是必要的。在本文中，我们讨论了由于采场顶板崩塌引起的地面沉降预测，重点是使用长壁法采煤。这项研究的主要目的是提高由于采矿引起的地面沉降预测模型的准确性。为此，我们考虑了与采煤有关的 11 个参数，包括采煤厚度和深度（与矿床有关），以及密度、内聚力、内摩擦角、弹性模量、体积模量、剪切模量、泊松比、单轴抗压强度和抗拉强度（与覆盖层有关）。我们利用从 14 个煤矿收集到的关于采煤和沉降的信息来实现这一目标。然后，我们使用基因表达编程（GEP）算法，通过人工蜂群（ABC）和蚁狮优化器（ALO）算法的组合进行了优化，探讨了采煤引起的沉降预测。建模结果表明，将 GEP 算法与基于 ABC 算法的优化相结合，可以实现最佳的沉降预测，相关系数达到 0.96。此外，敏感性分析表明，采煤深度和密度对使用长壁法采煤引起的地面沉降分别有最大和最小的影响。
