This paper presents the results of a comparative analysis of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) models created for the prediction of the extent and intensity of damage caused to multi-storey reinforced concrete (RC) buildings. The research was conducted on a group of residential buildings, which were subjected to mining impacts in the form of surface deformations and rock mass tremors during their technical life cycle. Damage to buildings poses a significant threat to the safety of the structure and the serviceability of the buildings. They are often the cause of breaks in thermal insulation, which leads to excessive consumption of thermal energy used for space heating, which in turn contributes to over-emissions of CO₂ into the atmosphere. Therefore, this problem is important, not only from a technical dimension, but also includes social, economic, and environmental aspects, which allows it to be classified as an issue of sustainable development in the building industry. As a result of the conducted analysis, among the CNN models, the highest level of classification accuracy was the model obtained using the ADAM (derived from adaptive moment estimation) algorithm, which was also characterized by a very high level of generalization, obtaining 80.35% correctly classified patterns for the training set and 80.52% for the test set. However, its accuracy level was slightly lower than that of the SVM model (85.15% for the training set and 84.42% for the test set), in which Bayesian optimization was used to determine the parameters. The analysis confirmed the effectiveness of the adopted methodology for predicting the extent and intensity of damage. The developed tool can support the optimization of building maintenance management, resulting in reduced economic and environmental expenditures for renovations.

The paper presents the research methodology aimed at determining the building damage intensity index as a linear combination of indices describing the damage to its individual components. The research base comprised 129 building structures erected in the large-block technology. The study compared the results of a standardized approach to data mining - PCA (Principal Components Analysis) with the procedure of the PLSR method (Partial Least Squares Regression). As a result of the analysis, a generalized form of the building damage index was obtained, as a linear combination of the damage to its components.

This article presents the results of the analysis of the extent of damage to 138 multi-storey buildings with reinforced concrete prefabricated structure, which are located in the mining terrain of the Legnica-Głogów Copper District. These objects are residential and public utility buildings of up to 43 years old, erected in industrialized prefabricated technologies: large-block and large-panel systems. The research was based on the results of technical condition inventory carried out in 2002, 2007 and 2012. As part of the analysis, the damage intensity index w_u was established for individual structural and finishing elements of the studied buildings. This index is defined on a six-point scale, which includes a detailed description of the extent of damage that corresponds to the successive degrees of intensity. As part of the research, the databases were significantly expanded and the generalized formulas of the damage intensity index w_u for individual groups of buildings were verified. For this purpose, the partial least squares regression (PLSR) method was applied. Thereafter, the analysis of changes of this intensity in time was carried out and the relations between the extent of damage and the impacts of mining exploitation were examined. The approach presented in this paper and obtained research results are characterized by a high degree of utilitarianism and can be applied to increase the efficiency in the optimal maintenance management of buildings, including planning of repairs and retrofits throughout the technical life cycle of the buildings.

The article presents a preliminary database analysis regarding the technical condition of 94 portal frame buildings located in the mining area of Legnica-Głogów Copper District (LGOM), using the methodology of decision trees. The scope of the analysis was divided into two stages. The first one included creating a decision tree by a standard CART method, and determining the importance of individual damage indices in the values of the technical wear of buildings. The second one was based on verification of the created decision tree and the importance of these indices in the technical wear of buildings by means of a simulation of individual dendritic models using the method of random forest. The obtained results confirmed the usefulness of decision trees in the early stage of data analysis. This methodology allows to build the initial model to describe the interaction between variables and to infer about the importance of individual input variables.

The article presents the case study of a historical religious building located in the area of “Bogdanka” S.A. Coal Mine. As the building lacked adequate resistance to the expected effects of mining, the Mine undertook efforts so that it was fully protected against mining impacts before the commencement of mining operations. A preventive conservation system was used, consisting of an external stiffening reinforced concrete plate at the ground level and a system of steel tie rods established at the level of vault supports. The article assesses the effect of undertaken preventive conservation measures on the current technical condition of the building. The basis for the assessment was the extent of damage to the building confirmed after the occurrence of impacts from the performed mining operations combined with the analysis of ground deformation.