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

Abstract

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.

Application of Bayesian networks for the prediction of gas accidents in semi-mechanized underground operations on the southern coast of Peru

“…The variation is produced by the number of years of analysis and the inclusion of geological factors typical of the operations analyzed. In addition, [15] defined that the application of Bayesian networks as a predictive model in research is vital since it allows emulating future behaviors and acting preventively. They established that generating more than 3 iterations in the network can generate a successful Bayesian inference (ROC value of 0.235).…”

“…However, the authors define that greater mathematical rigor should be added to the probabilities associated with the network since they also start from a subjective component. With respect to the ideal tool that can be coupled to Bayesian networks [12], [13], [14] and [15] agree that the establishment of statistical components that make it possible to predict future accidents in mining operations must be linked to artificial intelligence. To do this, they established different models that identify which are the missing points in accident investigation.…”

Convolutional Neural Network and Support Vector Machine for Prediction of Damage Intensity to Multi-Storey Prefabricated RC Buildings

“…Most of the contemporary research on building damage and its intensity is conducted by using conventional machine learning classifiers such as Support Vector Machines, Probabilistic Neural Networks or Random Forests [28,35,36]. Recently, Bayesian Belief Networks have also become very popular [3,37].…”

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

“…The Bayesian belief network can be interpreted as an acyclic directed acyclic graph (DAG), which consists of nodes (variables) and the edges connecting them [46][47][48].…”