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The Q-Slope Classification System, also known as the "Rock Mass Quality" system, was developed by Barton, Lien, and Lunde in 1974. It is commonly used in geotechnical engineering to assess the stability of slopes in various applications, including opencast mines. This system considers the quality of the rock mass and the condition of discontinuities (e.g., joints, faults, bedding planes) within the rock to evaluate slope stability. Here's how the Q-Slope Classification System can be applied to determine and analyze slope stability in opencast mines for sustainable mining and development:

By applying the Q-Slope Classification System in opencast mines, mining companies can make informed decisions about slope design, risk management, and ongoing monitoring to ensure safe and sustainable mining operations. This approach helps mitigate the potential environmental and safety hazards associated with open-pit mining activities.

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Stability Assessment of Rock Slopes Using the Q-Slope Classification System: A Reliability Analysis Employing Case Studies in Ecuador

by Cesar Borja Bernal, Ricardo Laín, Luis Jordá, Miguel Cano, Adrián Riquelme and Roberto Tomás

Appl. Sci. 2023, 13(13), 7399; https://doi.org/10.3390/app13137399

"Q-slope is one of the most recent empirical geomechanical classification systems and the least studied in South America. This study aims to expand the knowledge base regarding the Q-slope geomechanical method and demonstrate its applicability and reliability in rock slopes of Andean countries, such as Ecuador. To this end, thirty rocky slopes have been characterized considering (1) the physical visual approach—geographic location, climate, lithology, alteration, and stability (to obtain values of Jr, Ja, Jwice, and SRFa)—and (2) geomechanical stations and kinematic analysis (for the determination of the type of failure, Jn, O-factor, and SRFc for Q-slope). Field data were collected in contrasting environments (coastal, mountain, and forest), and different failure modes were considered (planar, wedge, block, and flexural topplings) to better understand the method. The results and main contributions of this research are (i) verifying the applicability of the Jwice parameters in different climatic settings and (ii) validating the Q-slope method by applying a confusion matrix to evaluate its reliability for slope stability assessment. The overall accuracy obtained is 80%, placing the Q-slope geomechanical method in the highest evaluation quartile and thus classifying it as very good for slope characterization.

Rock mass classifications have been successfully used worldwide for rock slope characterization for multiple reasons: effectiveness due to their connection with empirical methods and dissemination and communication across scientists, engineers, and the general public. In other words, rock mass classifications “put numbers to geology” [35]. The Q-slope method was applied herein in three natural regions of Ecuador—coast, mountains, and jungle—where igneous, sedimentary, and metamorphic rocks exhibiting different weathering conditions were found.

The result of the stability analysis using the Q-slope method determined that of the 30 slopes analyzed, 25 are in unstable conditions, 1 is almost stable, and 4 slopes are in stable conditions. The results demonstrated that instability processes occur for slopes starting at 61 degrees of inclination and 6 m high.

One of the main modifications implemented in the Q-method for its application to slopes is the shear strength, which, in our specific case, is obtained by multiplying the Jr/Ja ratio by the new O-factor. Nevertheless, when slopes that present toppling failures are studied, the method does not clearly establish the values to be considered for the O-factor (differently from the cases of planar and wedge failures). Therefore, future investigation is advisable to determine the O-factor values rigorously and accurately, especially for the toppling cases. The parameter could depend on the difference in strikes and dips between slope and joints, following the determination of the F1, F2, and F3 factors of the SMR correction.

Considering the values obtained and the metrics calculated for each class of statistical case a, it can be concluded that as sub-class 1 presents high values for precision and recall, the method can perfectly classify this sub-class. For sub-class 2, a high precision value is obtained with a low recall value; in this case, the method does not classify this class very well—however, when it does, it is highly reliable. For sub-class 3, the model provides a low precision value and a high recall value, which suggests that the method classifies the sub-class well but can also confuse the individuals of other sub-classes. The F1 score values obtained for each sub-class in the different statistical cases analyzed present values that range from 0.62 to 1, indicating that the Q-slope method is a balanced method that can confuse some cases as false but is also capable of correctly classifying all positive true cases, such as those of sub-class 1 of statistical case a. Only sub-class 2 of statistical case a returned an F1 score value of 0.25, which means that the model can confuse values as false for this specific case. Sub-classes 2 for statistical cases b and c present null values because the calculated FoS is a fixed value that only defines if the slope is stable or unstable. In these cases, a direct association could not be made between the quasi-stability obtained in situ and that predicted by the empirical method and FoS.

In general, considering that overall accuracy can be divided into quartiles, classifying quartile C1 as bad, C2 as intermediate, C3 as good, and C4 as very good, the value obtained for OA herein occupies the highest quartile, ranking the method as very good."