Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers
Abstract
:1. Introduction
2. Methods
2.1. Critical Span Graph and Problem Statement
2.1.1. Critical Span Graph
- Stable excavations: (i) no uncontrolled falling of the ground; (ii) no observed movement in the roof; and (iii) no extraordinary support measures implemented.
- Potentially unstable excavations: (i) extra ground support has been installed to prevent potential falling of the ground; (ii) movement of 1 mm or more in 24 h has been observed in the roof; or (iii) an increase in the frequency of popping and cracking indicating ground movement.
- Unstable excavations: (i) the area has collapsed; (ii) the depth of failure of the roof is -times the span (in absence of structure-related failure); or (iii) support was not effective in maintaining stability.
2.1.2. Problem Statement
2.2. Support Vector Machine Classifiers
2.3. Extreme Learning Machine Classifiers
3. Calculations and Results
3.1. Calculations
3.1.1. Database
3.1.2. Imbalanced Data
- Preprocessing strategies: These are based on sampling techniques that rebalance the dataset in order to get a new training set where the smallest class is better represented. Over-sampling, where new instances are created, can be used.
- Training strategies: These procedures modify the training incorporating information related with the proportion of number of samples between the classes. For example, to the extent that a learning classifier includes a cost function associated with the misclassification of the samples, a higher weight can be associated with the cost for the misclassification of samples from the smaller classes. This weight can also be related to the risk associated with a misclassification.
- Post-processing strategies: In general, these procedures are directed towards changing the weight vector of the decision function or of the determination of a new bias or threshold, in order to adjust the boundary decision by the learning classifier, so providing a good margin for separating the smallest class.
3.1.3. Classification with Support Vector Machine
Classification into Three Groups
Binary Probabilistic Classification
3.1.4. Classification with Extreme Learning Machine into Three Groups
3.2. Results
3.2.1. Results of Classification with Support Vector Machine
Classification into Three Groups
Binary Probabilistic Classification
3.2.2. Results of Classification with Extreme Learning Machine
4. Discussion
5. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Category | Main Zone | Talc Zone | ||
---|---|---|---|---|
Description | Rating | Description | Rating | |
Strength | 160–180 MPa | 13 | 35–50 MPa | 4 |
RQD | 90% | 17 | 80% | 16 |
Joint Spacing | 0.4 m | 16 | 0.3 m | 9 |
Joint Condition | smooth, hard, tight | 17 | smooth surfaces, soft | 10 |
Groundwater | none | 10 | none | 10 |
Joint Orientation | 0 | 0 | ||
Total RMR | 73 | 49 |
Mines | Cases | Stable (S) | Potentially Unstable (P) | Unstable (U) |
---|---|---|---|---|
Detour Lake Mine | 172 | 94 | 37 | 41 |
Detour Lake Mine | 22 | 10 | 0 | 12 |
Photo Lake Mine | 6 | 0 | 6 | 0 |
Olympias Mine | 13 | 4 | 1 | 8 |
Brunswick Mining | 17 | 5 | 3 | 9 |
Musslewhite Mine | 46 | 35 | 10 | 1 |
Snip Mine | 16 | 12 | 2 | 2 |
Red Lake Mine | 107 | 81 | 19 | 7 |
Summary | 399 | 241 | 78 | 80 |
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García-Gonzalo, E.; Fernández-Muñiz, Z.; García Nieto, P.J.; Bernardo Sánchez, A.; Menéndez Fernández, M. Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers. Materials 2016, 9, 531. https://doi.org/10.3390/ma9070531
García-Gonzalo E, Fernández-Muñiz Z, García Nieto PJ, Bernardo Sánchez A, Menéndez Fernández M. Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers. Materials. 2016; 9(7):531. https://doi.org/10.3390/ma9070531
Chicago/Turabian StyleGarcía-Gonzalo, Esperanza, Zulima Fernández-Muñiz, Paulino José García Nieto, Antonio Bernardo Sánchez, and Marta Menéndez Fernández. 2016. "Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers" Materials 9, no. 7: 531. https://doi.org/10.3390/ma9070531
APA StyleGarcía-Gonzalo, E., Fernández-Muñiz, Z., García Nieto, P. J., Bernardo Sánchez, A., & Menéndez Fernández, M. (2016). Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers. Materials, 9(7), 531. https://doi.org/10.3390/ma9070531