The method of strengthening the church building in terms of the planned mining exploitation

Drobiec, Łukasz; Niemiec, Tomasz; Kawulok, Marian; Słowik, Leszek; Chomacki, L.

doi:10.1051/matecconf/201928405003

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“…Usually, these take the form of large-scale continuous deformations [1,2], mining tremors [3,4] and local discontinuous deformations [5,6]. All these phenomena are a potential threat both in the context of safety [7,8], and often they are the cause of a significant reduction in the utility of buildings [9,10]. Regarding both of these issues, there have been efforts for many years to optimize the possibility of extraction of resources with the lowest possible degree of degradation of the existing buildings on the surface of the mining area.…”

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confidence: 99%

Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation

2021

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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.

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