https://www.cdc.gov/niosh/mining/UserFiles/works/pdfs/psius.pdf

Pillar strength in underground stone mines in the United States

The prediction of pillar stability (PS) in hard rock mines is a crucial task for which many techniques and methods have been proposed in the literature including machine learning classification. In order to make the best use of the large variety of statistical and machine learning classification methods available, it is necessary to assess their performance before selecting a classifier and suggesting improvement. The objective of this paper is to compare different classification techniques for PS detection in hard rock mines. The data of this study consist of six features, namely pillar width, pillar height, the ratio of pillar width to its height, uniaxial compressive strength of the rock, pillar strength, and pillar stress. A total of 251 pillar cases between 1972 and 2011 are analyzed. Six supervised learning algorithms, including linear discriminant analysis, multinomial logistic regression, multilayer perceptron neural networks, support vector machine (SVM), random forest (RF), and gradient boosting machine, are evaluated for their ability to learn for PS based on different input parameter combinations. In this study, the available data set is randomly split into two parts: training set (70 %) and test set (30 %). A repeated tenfold cross-validation procedure (ten repeats) is applied to determine the optimal parameter values during modeling, and an external testing set is employed to validate the prediction performance of models. Two performance measures, namely classification accuracy rate and Cohen’s kappa, are employed. The analysis of the accuracy together with kappa for the PS data set demonstrates that SVM and RF achieve comparable median classification accuracy rate and Cohen’s kappa values. All models are fitted by “R” programs with the libraries and functions described in this study.

Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction

Predicting pillar stability is a vital task in hard rock mines as pillar instability can cause large-scale collapse hazards. However, it is challenging because the pillar stability is affected by many factors. With the accumulation of pillar stability cases, machine learning (ML) has shown great potential to predict pillar stability. This study aims to predict hard rock pillar stability using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms. First, 236 cases with five indicators were collected from seven hard rock mines. Afterwards, the hyperparameters of each model were tuned using a five-fold cross validation (CV) approach. Based on the optimal hyperparameters configuration, prediction models were constructed using training set (70% of the data). Finally, the test set (30% of the data) was adopted to evaluate the performance of each model. The precision, recall, and F1 indexes were utilized to analyze prediction results of each level, and the accuracy and their macro average values were used to assess the overall prediction performance. Based on the sensitivity analysis of indicators, the relative importance of each indicator was obtained. In addition, the safety factor approach and other ML algorithms were adopted as comparisons. The results showed that GBDT, XGBoost, and LightGBM algorithms achieved a better comprehensive performance, and their prediction accuracies were 0.8310, 0.8310, and 0.8169, respectively. The average pillar stress and ratio of pillar width to pillar height had the most important influences on prediction results. The proposed methodology can provide a reliable reference for pillar design and stability risk management.

https://www.mdpi.com/2227-7390/8/5/765/pdf

Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

To understand the fracture mechanism in all kinds of rock engineering, it is important to investigate the fracture evolution behavior of pre-fissured rock In this research, we conducted uniaxial compression experiments to evaluate the influence of ligament angle on the strength, deformability, and fracture coalescence behavior of rectangular prismatic specimens (80 × 160 × 30 mm) of brittle sandstone containing two non-coplanar fissures The experimental results show that the peak strength of sandstone containing two non-coplanar fissures depends on the ligament angle, but the elastic modulus is not closely related to the ligament angle With the increase of ligament angle, the peak strength decreased at a ligament angle of 60°, before increasing up to our maximum ligament angle of 120° Crack initiation, propagation, and coalescence were all observed and characterized from the inner and outer tips of pre-existing non-coplanar fissures using photographic monitoring Based on the results, the sequence of crack evolution in sandstone containing two non-coplanar fissures was analyzed in detail In order to fully understand the crack evolution mechanism of brittle sandstone, numerical simulations using PFC2D were performed for specimens containing two non-coplanar fissures under uniaxial compression The results are in good agreement with the experimental results By analyzing the stress field, the crack evolution mechanism in brittle sandstone containing two non-coplanar fissures under uniaxial compression is revealed These experimental and numerical results are expected to improve the understanding of the unstable fracture mechanism of fissured rock engineering structures

An Experimental and Numerical Study on Cracking Behavior of Brittle Sandstone Containing Two Non-coplanar Fissures Under Uniaxial Compression

Assessment of mining induced stress development over coal pillars during depillaring

Despite more than half a century of experience with roof bolting, no design method has received wide acceptance. To begin to improve this situation, National Institute for Occupational Safety and Health (NIOSH) conducted a statistical study of roof bolt performance at a number of mines throughout the U.S. Case histories were collected from 37 mines with a variety of roof bolt types and patterns in a wide range of geologic environments. Performance was measured in terms of the number of roof falls that occurred per 10,000 ft of drivage. The study found that roof falls are rare when the roof is strong and the stress is low, even with light roof bolting patterns. The focus of this paper is on the more difficult conditions, where the roof is weaker and/or the stress is higher. Analysis of the results led to guidelines that can be used to make preliminary estimates of the required bolt length, capacity, and pattern. The guidelines are based on the depth of cover (which correlates with stress) and the roof quality (measured by the Coal Mine Roof Rating (CMRR), and the intersection span. Another contribution is a formula for estimating the horizontal stress level in the eastern U.S. coalfields as a function of the depth of cover. The design guidelines are currently being implemented into a computer program called Analysis of Roof Bolt Systems (ARBS).

/pdf/analysis-of-roof-bolt-systems-3sue6521l2.pdf

Analysis of Roof Bolt Systems.

Fully-grouted roof bolts comprise more than 80% of the primary roof supports used in U.S. coal mines. However, nearly 1,500 MSHA reportable, non-injury roof falls occur each year, and most of these are attributable to failure of the roof bolt system. Anchorage failure is one failure mechanism for fully-grouted bolts. As roof deformation works its way upward, the bolts can become heavily loaded near their upper ends. If the applied load exceeds the anchorage, the bolts will simply pull out. Research dating back 30 yrs indicates that this type of anchorage failure is most likely when the roof rock is weak, just where roof support is most critical. In soft shale or coal roof, the small amount of data available in the literature indicates that 20-30 in of resin anchorage may be required to achieve the full capacity of the bolt. In other words, the “full resistance zone” of a 60 in bolt may actually be just 30-40 in. Despite its potential importance, there is no widely accepted anchorage test for fully grouted bolts. Standard pull tests have sometimes been employed, but they provide no information on the anchorage near the top of the bolt. An alternative, first described more than 25 yrs ago in the U.S., is the short-encapsulation pull test (SEPT). With this test, the bolt is installed with only a short (1 ft or less) tube of resin. In recent years variations of this test have become international standards. This paper describes recent studies using short encapsulation pull tests in the U.S. Tests were conducted in the National Institute for Occupational Safety and Health (NIOSH) Safety Research Coal Mine at Bruceton and at underground mines in Pennsylvania and West Virginia. The study found that the SEPT can be used to make a simple evaluation of resin bolt anchorage. Suggested procedures for conducting SEPT are included. The study also confirmed that poor anchorage can be an issue, particularly where the roof rock is very weak. Some simple techniques for improving anchorage, and thereby the effectiveness of fully grouted bolts, are discussed.

/pdf/anchorage-pull-testing-for-fully-grouted-roof-bolts-ewujqsok90.pdf

Anchorage Pull Testing For Fully Grouted Roof Bolts

/pdf/pillar-and-roof-span-design-guidelines-for-underground-stone-35w1rkq6tn.pdf

Pillar and roof span design guidelines for underground stone mines

/pdf/assessing-coal-mine-roof-stability-through-roof-fall-3xb3z3nppc.pdf

Dennis R. Dolinar

Papers

Pillar strength in underground stone mines in the United States

Analysis of Roof Bolt Systems.

Anchorage Pull Testing For Fully Grouted Roof Bolts

Pillar and roof span design guidelines for underground stone mines

Assessing Coal Mine Roof Stability Through Roof Fall Analysis