Underground Excavation Behaviour of the Queenston Formation: Tunnel Back Analysis for Application to Shaft Damage Dimension Prediction

Perras, Matthew A.; Wannenmacher, Helmut; Diederichs, Mark S.

doi:10.1007/s00603-014-0656-z

Cited by 25 publications

(10 citation statements)

References 26 publications

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“…A typical approach is to use the back analysis of numerical models to calibrate their behaviour against observed site conditions. These calibrated models are then used to forward predict the excavation behaviour as the excavation is advanced, or as a new excavation is being designed in the same rock mass but at a different location [13]. Figure 1 illustrates this process, summarized as follows:…”

Section: Methodsmentioning

confidence: 99%

“…Comparison with empirical approaches [14] to validate the new design. An illustration of a numerical modelling approach used to predict the overbreak depth around a vertical shaft (modified from [13]). Step 1.…”

Section: Methodsmentioning

confidence: 99%

“…Step 1. Observed overbreak (yellow dashed line) beyond the designed perimeter (solid red line) of the tunnel [13].…”

Section: Methodsmentioning

confidence: 99%

“…Step 4. Comparison of calibrated models and conceptual behaviour to an empirical approach [14] for predicting normalized overbreak depth (r/R) using the maximum tangential stress (σmax) calculated from the maximum and minimum horizontal stresses, σH and σh, respectively ( [13,18]). Figure 1.…”

Section: Methodsmentioning

confidence: 99%

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An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

2019

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Machine learning methods for data processing are gaining momentum in many geoscience industries. This includes the mining industry, where machine learning is primarily being applied to autonomously driven vehicles such as haul trucks, and ore body and resource delineation. However, the development of machine learning applications in rock engineering literature is relatively recent, despite being widely used and generally accepted for decades in other risk assessment-type design areas, such as flood forecasting. Operating mines and underground infrastructure projects collect more instrumentation data than ever before, however, only a small fraction of the useful information is typically extracted for rock engineering design, and there is often insufficient time to investigate complex rock mass phenomena in detail. This paper presents a summary of current practice in rock engineering design, as well as a review of literature and methods at the intersection of machine learning and rock engineering. It identifies gaps, such as standards for architecture, input selection and performance metrics, and areas for future work. These gaps present an opportunity to define a framework for integrating machine learning into conventional rock engineering design methodologies to make them more rigorous and reliable in predicting probable underlying physical mechanics and phenomenon.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Step 1. Observed overbreak (yellow dashed line) beyond the designed perimeter (solid red line) of the tunnel [13].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

2019

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“…With increase in demand for resources and energy, the relevant rock engineering activities, such as deep mining and development of geothermal resources, have increased [1][2][3][4][5][6][7]. e cracking process in quasibrittle materials such as rocks and concretes in geotechnical engineering is usually characterized by the formation of microcracks that eventually merge and form a propagating macrocrack.…”

Section: Introductionmentioning

confidence: 99%

Microscopic Investigation of Rate Dependence on Three‐Point Notched‐Tip Bending Sandstone

Shaikh

Wang

et al. 2019

Shock and Vibration

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The cracking process in rock or concrete is usually characterized by the formation of microcracks that eventually form a propagating macrocrack. A series of three-point bending experiments were performed on sandstone containing Mode I crack under different loading rates. The microscopic monitoring system was established to capture the cracking process at notch tip. The loading rate dependence of microcracking behaviour was analysed based on load-time curves, acoustic emission (AE), microscopic images, and micrograph-based digital image correlation (DIC) technology. Results showed that the specimens underwent a short period of compression and elastic deformation stage under high loading rate, and the peak loads increased with the increase in loading rate. The AE results revealed that the fracturing process can be divided into elastic stage, damage stage, and postpeak stage, and more extensive damage occurred before the peak under low loading rate. It can be observed from microscopic images that the crack was initiated during the elastic stage, which was earlier than that determined from the AE monitoring. In addition, the microcracks were initiated at multiple locations and were mainly located at the interfaces between dense grains under low loading rate, while microcracks were observed inside the grains under high loading rate. Furthermore, the DIC results showed that the crack opening displacement (COD) of 0.6 mm/min at the peak was almost twice than that of 3.0 mm/min. The COD under the same loading rate at the peak can be considered as the material property of sandstone.

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Practical recommendations for machine learning in underground rock engineering – On algorithm development, data balancing, and input variable selection

Morgenroth

Unterlaß

Sapronova

et al. 2022

Geomechanics and Tunnelling

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Research has demonstrated that machine learning algorithms (MLAs) are a powerful addition to the rock engineering toolbox, and yet they remain a largely untapped resource in engineering practice. The reluctance to adopt MLAs as part of standard practice is often attributed to the ‘opaque’ nature of the algorithms, the complexity in developing them, and the difficulty in determining how the algorithms use the datasets. This article presents tools and processes for developing MLAs, input selection, and data balancing for practical underground rock engineering. MLAs for classification and regression – two main machine learning applications – are presented in terms of developing MLA to extract information from the dataset to obtain the desired output. Engineering verification metrics are selected based on their suitability for specific output. Methods for input selection and data balancing are discussed with a focus on selecting appropriate input data for the problem without introducing bias or excess complexity. Each tool and process for algorithm development, data preparation, and input selection is illustrated with a case study. This article demonstrates that geotechnical practitioners can extract additional value by applying MLAs to rock engineering problems. Once an understanding of the functions of MLAs is reached, the building blocks and open‐source code are available to be adapted to suit the rock mass behaviour of interest.

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Underground Excavation Behaviour of the Queenston Formation: Tunnel Back Analysis for Application to Shaft Damage Dimension Prediction

Cited by 25 publications

References 26 publications

An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

Microscopic Investigation of Rate Dependence on Three‐Point Notched‐Tip Bending Sandstone

Practical recommendations for machine learning in underground rock engineering – On algorithm development, data balancing, and input variable selection

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