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

Morgenroth, Josephine; Khan, Usman T.; Perras, Matthew A.

doi:10.3390/geosciences9120504

Cited by 54 publications

(20 citation statements)

References 74 publications

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“…In tunnels, engineers often interpret possible patterns in data for decisionmaking in a stressed environment. In today's rock engineering industry, expert judgement has the highest credence and empirical relationships form the foundation of rock engineering methods [8]. These are not optimum processes.…”

Section: Handling Of Uncertainties Using Digital Toolsmentioning

confidence: 99%

Norwegian tunnel excavation: Increasing digitalisation in all operations

Chiu

Hansen

Wetlesen³

2022

Geomechanics and Tunnelling

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In the past decades, Norwegian tunnelling has undertaken a major digital development in all operations from planning to the excavation phase. The breakthrough is driven by digital contract requirements from forward‐leaning clients and innovative industrial stakeholders, aiming for ever more efficient, quality‐oriented and risk‐reducing operations. A formal milestone documenting this development was the publication of Digitalisation in Norwegian Tunnelling in 2019. From 2019, most contracts of new infrastructure tunnels must deliver a level 3 Building Information Modeling (BIM) model. Control systems for drilling jumbos are fully digitalised. All drilling must be monitored and documented with Measurement While Drilling techniques. Via cloud servers, the drilling information is interpreted and delivered live to face engineers for decision support. Sensors are used to monitor rock grouting flow and pressure in each drill hole. Digital electronic detonators with exact delay time are used for blasting. Geotechnical mapping is carried out on field tablets. High‐resolution scans with RGB imaging must be carried out on the exposed rock surface and after rock support/linings. The next step is to utilise all the collected data to a higher degree by advanced analysis with machine learning (ML) and similar techniques for automation and optimisation. This study reviews and exemplifies the digital focus and achievements in core operations in Norwegian tunnel excavation.

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Section: Handling Of Uncertainties Using Digital Toolsmentioning

confidence: 99%

Norwegian tunnel excavation: Increasing digitalisation in all operations

Chiu

Hansen

Wetlesen³

2022

Geomechanics and Tunnelling

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“…Despite the fact that machine learning has been successfully used in a broad range of areas over the last decades, its utilization in the field of rock engineering is relatively new. Morgenroth (2019) states that machine learning can be a valuable tool to be integrated into the rock engineering practices, due to the complex nature of the geotechnical problems, the difficulty in utilizing all geotechnical data into empirical and numerical models and the rapid increase of the collected data. McGaughey (2019) stated that the application of artificial intelligence in the field of rock engineering is not a simple task, because the data required to make a prediction are sparsely scattered in space and time.…”

Section: Machine Learning In Rockburst Predictionmentioning

confidence: 99%

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

Papadopoulos

Benardos

2021

Geotech Geol Eng

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One of the main challenges that deep mining faces is the occurrence of rockburst phenomena. Rockburst risk assessment with the use of machine learning is currently gaining increased attention, due to the fact that outperforms the widely used empirical approaches. However, the limited and imbalanced instance records, combined with the multiparametric nature of the phenomenon, can lead to unstable estimations. This study focuses on the enhancement of the prediction performance of five machine learning algorithms, including Decision Trees, Naïve Bayes, K-Nearest Neighbor, Random Forest and Logistic Regression, by utilizing the oversampling technique SMOTE (Synthetic Minority Oversampling TEchnique).The initial database consists of 249 rockburst incidents, from which approximately 70% was used as the training set and the remaining 30% as the test set. Parametric analyses were conducted regarding different indicator combinations, such as the maximum tangential stress, the rock's uniaxial compressive and tensile strength, the stress coefficient, two brittleness coefficients and the elastic energy index. The models were trained with the original dataset and afterwards a gradual increase of the database with synthetic instances was made until the obtainment of a balanced dataset. Subsequently the creation of synthetic instances was continued until the real incidents used for training and the synthetic incidents were of the same amount. The results from the following analysis show that SMOTE technique has a considerable effect in the evaluation metrics of the models, even after the balancing of the dataset, and can be a valuable asset for the rockburst prediction.

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“…ANNs have been widely used in various geotechnical applications [21], such as in prediction of pile capacity [22][23][24][25][26][27][28][29][30], constitutive modeling of soil [31][32][33][34][35][36][37], site characterization [38,39], earth-retaining structures [40], settlement of foundations [41,42], prediction of unknown foundations [43], slope stability [44,45], design of tunnels and underground openings [46,47], liquefaction [48][49][50][51][52], soil permeability and hydraulic conductivity [53], soil compaction [54,55], and soil classification [56,57].…”

Section: Introductionmentioning

confidence: 99%

Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning

et al. 2021

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Predicting the range of achievable strength and stiffness from stabilized soil mixtures is critical for engineering design and construction, especially for organic soils, which are often considered “unsuitable” due to their high compressibility and the lack of knowledge about their mechanical behavior after stabilization. This study investigates the mechanical behavior of stabilized organic soils using machine learning (ML) methods. ML algorithms were developed and trained using a database from a comprehensive experimental study (see Part I), including more than one thousand unconfined compression tests on organic clay samples stabilized by wet soil mixing (WSM) technique. Three different ML methods were adopted and compared, including two artificial neural networks (ANN) and a linear regression method. ANN models proved reliable in the prediction of the stiffness and strength of stabilized organic soils, significantly outperforming linear regression models. Binder type, mixing ratio, soil organic and water content, sample size, aging, temperature, relative humidity, and carbonation were the control variables (input parameters) incorporated into the ML models. The impacts of these factors were evaluated through rigorous ANN-based parametric analyses. Additionally, the nonlinear relations of stiffness and strength with these parameters were developed, and their optimum ranges were identified through the ANN models. Overall, the robust ML approach presented in this paper can significantly improve the mixture design for organic soil stabilization and minimize the experimental cost for implementing WSM in engineering projects.

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

Cited by 54 publications

References 74 publications

Norwegian tunnel excavation: Increasing digitalisation in all operations

Norwegian tunnel excavation: Increasing digitalisation in all operations

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning

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