This paper presents an advanced computational approach to assess the risk of damage to masonry buildings subjected to negative kinematic impacts of underground mining exploitation. The research goals were achieved using selected tools from the area of artificial intelligence (AI) methods. Ultimately, two models of damage risk assessment were built using the Naive Bayes classifier (NBC) and Bayesian Networks (BN). The first model was used to compare results obtained using the more computationally advanced Bayesian network methodology. In the case of the Bayesian network, the unknown Directed Acyclic Graph (DAG) structure was extracted using Chow-Liu’s Tree Augmented Naive Bayes (TAN-CL) algorithm. Thus, one of the methods involving Bayesian Network Structure Learning from data (BNSL) was implemented. The application of this approach represents a novel scientific contribution in the interdisciplinary field of mining and civil engineering. The models created were verified with respect to quality of fit to observed data and generalization properties. The connections in the Bayesian network structure obtained were also verified with respect to the observed relations occurring in engineering practice concerning the assessment of the damage intensity to masonry buildings in mining areas. This allowed evaluation of the model and justified the utility of the conducted research in the field of protection of mining areas. The possibility of universal application of the Bayesian network, both in the case of damage prediction and diagnosis of its potential causes, was also pointed out.

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.

The paper describes an example of a failure of an office building in Upper Silesia, Poland. Following a technical description of the building, the authors describe the damage to the building structure and damage development. The causes of damage of the building structure are assessed and discussed. The main cause is a long-term failure of the water supply system that resulted in deterioration in the foundation subsoil. The investigations also showed previously unidentified subsoil deformation left after old mining exploitation. The paper concludes with the discussion of the ongoing restoration of the technical efficiency of the building.

Mining tremors may have an impact on the safety risk of steel pipelines through their effects. It is therefore important to quantify the impact of a high-energy mining tremor in terms of strength. In addition, a comparison of the results obtained with the effect of a seismic tremor can illustrate the scale of such a hazard. Recently, this has been a very frequently raised issue in the area of surface protection against negative mining impacts and the protection of post-mining areas. Ensuring safe use is particularly important for gas transmission elements. This paper presents the results of a comparative analysis of the impact of mining tremors and seismic impacts on a specimen steel pipeline segment. The analyzed pipeline is located in the eastern part of Poland in the area of paraseismic impacts of the LGCD (Legnica-Glogow Copper District) mine. For this purpose, an analytical approach was used to assess the impact of seismic wave propagation on underground linear infrastructure facilities. Accelerogram records for the 02-06-2023 seismic tremor from Turkey and the mining tremor for 11-25-2020 were used. In the case of the design of underground pipelines, the cross-section of the element for which measures describing wall stress and the ovalization of the cross-section are determined is usually considered. In the situation of the influence of seismic wave propagation or so-called permanent ground deformation, the response of the pipeline in the longitudinal direction is analyzed. As a final result, longitudinal strains transferred to the pipeline as a consequence of the propagating seismic wave and mining tremor were determined. The absolute difference between the deformations in the ground and along the length of the pipeline was determined. This type of analysis has not been carried out before and provides new insights into the topic of paraseismic impacts on the scale of their interaction with natural earthquakes. Mining tremor data was obtained from the mine’s seismological department. The seismic tremor data, on the other hand, was downloaded via the publicly available ESM (Engineering Strong- Motion Database).