... ABSTRACT The uncertainty of rockmass conditions will be an important factor causing TBM construction risks and inefficient tunnelling. Currently, prediction of rockmass classification based on TBM operation data and machine learning models has been proved feasible by many researchers. However...

... stability analysis classification mining science bieniawski ARMA 23 0747 Slope stability analysis and design for small-scale mines Mgiba, C. University of Johannesburg, Johannesburg, Gauteng, South Africa Rupprecht, SM. University of Johannesburg, Johannesburg, Gauteng, South Africa Copyright 2023...

... united states government geologist asia government geology upstream oil & gas deep learning artificial intelligence china government mechanism circulation classification input feature interpretability artificial neural network accuracy halstm model drilling operation...

... training dataset geologist rock type fold and thrust belt zonation karewa basin classification indian standard ABSTRACT Presently slope stability analysis and landslide hazard monitoring are the most challenging tasks in mountainous regions like the Himalayas. These events which were earlier...

... ABSTRACT Detecting the damaged precursor of deep tunnel excavations using crack classification is essential for ensuring structural safety and durability since the cracking mode of a crack (tensile or shear) is connected to a rockburst. Acoustic emission (AE) is related to the mechanisms...

... contributors to the wetmuck at PTFI. Other contributing factors include lithology, fragmentation and draw rates (See Section 4.1 for detail Wetmuck Classification). As mining in the DOZ progressed, the number of drawpoints with a wetmuck class progressively increased, thereby impacting the blending material...

.... artificial intelligence dataset neural network machine learning probability correspond algorithm revisiting rockburst upstream oil & gas metals & mining classification rockburst engineering reservoir characterization adoko accuracy heal prediction underground space technology...

... by verifying the applicability of machine learning to rock surface evaluation. Support vector machine (SVM) is implemented to build the model for the classification of four support patterns based on data collected from different tunnel construction sites. As a result, the proposed model has shown high accuracy...

.... prediction accuracy estimation upstream oil & gas machine learning reservoir characterization predictor evaluation artificial intelligence strength application classification is50 rock strength classifier core logging data quantity pierce differentiate ARMA 22 283 Application...

... with multi-factor and multi-index. The study on the classification of rock masses has gradually changed from single index (RQD method) to multi-index (RMR method, BQ method, Q-system, etc.) (Cui et al., 2021; Hajiazizi and Sadat Khatami, 2013; Khatik and Nandi, 2018; Zhang et al., 2019). So, a comprehensive...

... characterization bieniawski shear strength tunnel stability himalaya wellbore design larger span overhanging cliff constructed strength upstream oil & gas span classification engineering assessment safety figure 6 modeling ARMA 22 0534 Design of Larger Span Overhanging Cliffs Constructed...

...ARMA 22 143 A Conventional Neural Network Lithology Classification Method Based on Vibration Data Gang Chen, Peng Tan CNPC Engineering Technology R&D Company Limited, Beijing, China Jiran Li Beijing Petroleum Machinery Co., Ltd., Beijing, China Hailong Jiang Xi an Petroleum University, Xi an, China...

... of fault/shear zones and some examples are given using some rock classifications. Then, some direct shear tests were performed on the actual fault gouges and their actual characteristics have been evaluated. The characterization and evaluation of the properties of fault/shear zones using RMQR rock...

... of the predictions with the actual data. Besides, supervised classification of the geological layers was done using decision trees and support vector machine algorithms. The optimum number of branches that construct the decision tree is found to be 20. The best quality of fitting was obtained using decision trees...

... of the slope. Mountainous slopes are large and are sinuous in terms of sloping direction. lithology criticality envelop discontinuity karchham orientation continuous kinematic criticality classification sarkar reservoir characterization discontinuity orientation approach rahman niyogi...

...ARMA 21 1623 Classification and mechanism analysis of wellbore failure based on cavings Jian Jin1, Yan Jin1 Yunhu Lu1, Huiwen Pang1,2 1 State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China 2 College of Science, China University...

... provided by transportation authorities and other governing bodies have been analyzed (McCauley et al., 1985; Delonca et al., 2014). us government rockfall activity artificial intelligence calculation classification rock slope machine learning rockfall bedrock floyd hill point cloud...

.... Two models were developed: a predictive (fitting) model and a classification model. The results indicate that ANN, as a prediction model, is not appropriate for dilution because of a low prediction capability observed (R 2 = 0.57). On the other hand, the classifier model showed good classification...

... underground excavation damages induced by mine seismicity in order to efficiently control a ground prone to rockburst. machine learning artificial intelligence reservoir characterization evolutionary algorithm upstream oil & gas classification performance corresponding classification rock...

... simulation fem model classification fem simulation framework model finite element run time workflow perturbation ABSTRACT: Geomechanical modeling of three-dimensional geologic structures is helpful for understanding fault related stress perturbations. The choice of which modeling technique...