Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Predicting Mining Areas Deformations under the Condition of High Strength and Depth of Cover

This paper presents an analysis of mining area deformations in the rock mass consisting of high depth and strength strata deposited in the cover. The analysis of land surveying results enabled the identification of the parameters required to predict subsidence, which differed from the typical parameters for the Upper Silesian Coal Basin. The parameters of the Budryk–Knothe theory were determined based on the results of geodetic measurements. The calculations of the final state of deformations for planned mining were made using the average and characteristics for the study area parameter values. Based on experience, it is known that the range of subsidence trough depends on the mechanical properties of the rock mass. This study shows that the presence of high-strength rocks also reduces the value of the coefficient of roof control. Subsequently, calculations were made by a computer simulation of longwall mining to determine the course of indices of deformation over time. The calculations were conducted twice: on the assumption that the impact was immediate and on the assumption of the parameter values typical for the basin, and formula expressing the course of subsidence over time with the parameter values based on the measurement results. The obtained distributions of deformation indicators were diametrically opposed to each other. The results of the calculations with the parameter values appropriate for the region indicate that it is possible to carry out a planned mining operation without creating a risk to objects on the surface.

Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

This paper presents the results of comparative studies on the implementation of machine learning methods in the damage intensity assessment of masonry buildings. The research was performed on existing residential buildings, subjected to negative impacts of the industrial environment induced by coal mining plants during their whole technical life cycle. The research was justified on the grounds of safety of use, as well as potential energy losses and CO2 emissions generated by the inefficient management of building materials resources resulting from poor planning of retrofitting. In this field, the research is in line with the global trends of large-scale retrofitting of existing buildings in European countries due to their thermal insulation parameters and seismic hazard. By combining this with the effects of material degradation throughout the technical lifecycle of buildings, the proposed methods allow for a more efficient approach to maintaining quality management of large groups of buildings, which is part of the sustainable development framework. Due to the multidimensionality of the undertaken problem and the necessity of mathematical representation of uncertainty, it was decided to implement a machine learning approach. The effectiveness of the following methods was analysed: probabilistic neural network, support vector machine, naive Bayes classification and Bayesian belief networks. The complexity of individual methods dictated the order of the adopted research horizon. Within such a research plan, both model parameters were learned, and model structure was extracted from the data, which was applied only to the approach based on Bayesian networks. The results of the conducted analyses were verified by assuming classification accuracy measures. Thus, a method was extracted that allows for the best realisation of the set research objective, which was to create a classification system to assess the intensity of damage to masonry buildings. The paper also presents in detail the characteristics of the described buildings, which were used as input variables, and assesses the effectiveness of the obtained results in terms of utilisation in practice.