Hafeezur Rehman; Wahid Ali; Kausar sultan Shah; Mohd Hazizan bin Mohd Hashim; Naseer Muhammad Khan; Muhammad Ali; Muhammad Kamran; Muhammad Junaid
Abstract
Support design is the main goal of the Q and rock mass rating (RMR) systems. An assessment of the Q and RMR system application in tunnelling involving high-stress ground conditions shows that the first system is more appropriate due to the stress reduction factor. Recently, these two systems have been ...
Read More
Support design is the main goal of the Q and rock mass rating (RMR) systems. An assessment of the Q and RMR system application in tunnelling involving high-stress ground conditions shows that the first system is more appropriate due to the stress reduction factor. Recently, these two systems have been empirically modified for designing the excavation support pattern in jointed and highly stressed rock-mass conditions. This research work aims to highlight the significance of the numerical modelling, and numerically evaluate the empirically suggested support design for tunnelling in such an environment. A typical horse-shoe-shaped headrace tunnel at the Bunji hydropower project site is selected for this work. The borehole coring data reveal that amphibolite and Iskere Gneiss are the main rock mass units along the tunnel route. An evaluation of the proposed support based on the modified empirical systems indicate that the modified systems suggest heavy support compared to the original empirical systems. The intact and mass rock properties of the rock units are used as the input for numerical modelling. From numerical modelling, the axial stresses on rock bolts, thrust bending moment of shotcrete, and rock load from modified RMR and Q-systems are compared with the previous studies. The results obtained indicate that the support system designed based on modified version of the empirical systems produce better results in terms of tunnel stability in high-stress fractured rock mass conditions.
M. Kamran; Sh. Bacha; N. Mohammad
Abstract
This paper elucidates a new idea and concept for exploration of the gold ore deposits. The cyanidation method is traditionally used for gold extraction. However, this method is laborious, time-consuming, costly, and depends upon the availability of the processing units. In this work, an attempt ...
Read More
This paper elucidates a new idea and concept for exploration of the gold ore deposits. The cyanidation method is traditionally used for gold extraction. However, this method is laborious, time-consuming, costly, and depends upon the availability of the processing units. In this work, an attempt is made in order to update the gold exploration method by the Monte Carlo-based simulation. An excellent approach always requires a high quality of the datasets for a good model. A total of 48 incomplete datasets are collected from the Shoghore district, Chitral area of Khyber, Pakhtunkhwa, Pakistan. The cyanidation leaching test is carried out in order to measure the percentage of the gold ore deposits. In this work, the mean, median, mode, and successive iteration substitute methods are employed in such a way that they can compute the datasets with missing attributes. The multiple regression analysis is used to find a correlation between the potential of hydrogen ion concentration (pH), solid content (in %), NaCN concentration (in ppm), leaching time (in Hr), particle size (in µm), and measured percentage of gold recovery (in %). Moreover, the normal Archimedes and exponential distributions are employed in order to forecast the uncertainty in the measured gold ore deposits. The performance of the model reveals that the Monte Carlo approach is more authentic for the probability estimation of gold ore recovery. The sensitivity analysis reveals that pH is the most influential parameter in the estimation of the gold ore deposits. This stochastic approach can be considered as a foundation to foretell the probabilistic exploration of the new gold deposits.
M. Kamran
Abstract
The blasting operation is an important rock fragmentation technique employed in several foundation engineering disciplines such as mining, civil, tunneling, and road planning. Back-break (BB) is one of the adverse effects caused by the blasting operations that produces several effects including vulnerability ...
Read More
The blasting operation is an important rock fragmentation technique employed in several foundation engineering disciplines such as mining, civil, tunneling, and road planning. Back-break (BB) is one of the adverse effects caused by the blasting operations that produces several effects including vulnerability of mining machinery, bench slope design, and risks to the next blast-patterns due to the eruption of gases from several discontinuities in jointed rock masses. Several techniques have been executed by the researchers in order to predict BB in the blasting operations. However, this is the first work to implement a-state-of-the-art Catboost-based t-distributed stochastic neighbor embedding (t-SNE) approach to predict BB. A total of 62 datasets having 12 influential BB-generating features are collected from genuine blasting patterns. A novel dimensionality depletion technique t-SNE that operates the Kullback-Leibler divergence interpretation is employed to tailor the pioneer exaggeration of the blasting dataset. Then the t-SNE dataset obtained is split into a 70:30 ratio of the training and testing datasets. Finally, the Catboost method is implemented on a low-dimensionality blasting database. The performance evaluation criterion confirms that the BB predictive model is more stable with a goodness of fit = 99.04 in the training dataset, 97.26 in the testing datasets, and could anticipate a more accurate prediction. Moreover, the model presented in this work performs superior to the existing publicly available execution of BB. In summary, this model can be practiced in order to predict BB in several rock engineering practices and mining industry scenarios.
M. Kamran
Abstract
Drillability is one of the significant issues in rock engineering. The drilling rate index (DRI) is an important tool in analyzing the drillability of rocks. Several efforts have been made by the researchers to correlate and evaluate DRI of rocks. The ensemble learning methods including the decision ...
Read More
Drillability is one of the significant issues in rock engineering. The drilling rate index (DRI) is an important tool in analyzing the drillability of rocks. Several efforts have been made by the researchers to correlate and evaluate DRI of rocks. The ensemble learning methods including the decision tree (DT), adaptive boosting (AdaBoost), and random forest (RF) are employed in this research work in order to predict DRI of rocks. A drillability database with four parameters is compiled in this work. A relationship between the input parameters and DRI is established using the simple regression analysis. In order to train the model, different mechanical properties of rocks incorporating the uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), brittleness test (S20), and sievers’ J-miniature drill value (Sj) are taken as the input variables. The original DRI database is randomly divided into the training and test sets with an 80/20 sampling method. Various algorithms are developed, and consequently, several approaches are followed in order to predict DRI of the rock samples. The model performance has revealed that RF predicts DRI with a high accuracy rate. Besides, the Monte Carlo simulations exhibit that this approach is more reliable in predicting the probability distribution of DRI. Therefore, the proposed model can be practiced for the stability risk management and the investigative design of DRI.
