Decision support system for underground coal pillar stability using unsupervised and supervised machine learning approaches

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f(1)-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

Automated summary

Coal pillar assessment is crucial for underground engineering structures due to the potential disasters caused by pillar failure. Traditional forecasting techniques are insufficient in generating accurate outcomes due to the non-linear correlation between pillar failure and its influential attributes. This paper proposes a new approach using combined unsupervised-supervised learning to forecast underground coal pillar stability. By building a database of authentic engineering structures and employing advanced feature depletion and clustering methods, the proposed model can accurately predict the class of pillar failure in various underground rock engineering projects.