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 article presents the results of the research on the construction of a model for assessing
the risk of damage to building structures located in mining areas. The research was based
on the database on the structure, technical condition and mining impacts regarding 129
prefabricated reinforced concrete buildings erected in the industrialised large-block system,
located in the mining area of the Legnica-Glogow Copper District (LGCD). The methodology of the Bayesian Belief Network (BBN) was used for the analysis. Using the score-based
Bayesian structure learning approach (Hill-Climbing and Tabu-Search) as well as the selected optimisation criteria, 16 Bayesian network structures were induced. All models were
subjected to quantitative and qualitative evaluation by verifying their features in the context
of accuracy of prediction, generalisation of acquired knowledge and cause-effect relationships. This allowed to select the best network structure together with the corresponding
optimisation criterion. The analysis of the results demonstrated that the Tabu-Search method
adopting the optimisation criterion in the form of Locally Averaged Bayesian Dirichlet score
(BDla) led to obtaining a model with the best features among all the selected models. The
results justified the adoption of the BBN methodology as effective in the context of assessing
the extent of damage to building structures in mining areas.
This article describes the method of strengthening a church building in Ruda Śląska against predicted effects of mining exploitation. The building had been already exposed to effects of 22 mining incidents which significantly damaged walls and vaults. Therefore, it was decided to strengthen the building before new exploitation works. Strengthening works included longitudinal walls support with the tendon system built over the vault support. Additionally, ribs were strengthened by suspending them to new reinforced concrete ribs placed over the existing ones. And precast concrete cube elements were used in new ribs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.