Estimation of tunnel support pattern selection using artificial neural network

Liu, Jiankang; Jiang, Yujing; Ishizu, Sodai; Sakaguchi, Osamu

doi:10.1007/s12517-020-05311-z

Cited by 10 publications

(12 citation statements)

References 58 publications

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“…In laboratory experiments drilling through prepared samples, it was demonstrated that rock properties, including density, porosity, Pwave velocity, Schmidt hardness, UCS, tensile strength and Elasticity Modulus, could be successfully predicted from acoustics produced during drilling of laboratory samples [62]. On the other hand in the tunnelling space, simulated fractures were detected in the rock mass from MWD data trends during the excavation of a 20 metre wide tunnel [53]. More recently, several Chinese tunnel excavations have used regression and classification-based methods to determine rock properties from MWD data.…”

Section: Commoditiesmentioning

confidence: 99%

“…Surface mine ML applications on MWD data were generally focused on geotechnical characterisation to improve fragmentation, or rock breakage from blasting [1,6,25,50]. The focus of understanding rock mass properties from MWD data in underground mining and tunnelling based research is to reduce strata failure by adjusting spacing and locations of ground support equipment [13,[51][52][53][54][55][56][57][58][59].…”

Section: Rock Mass Properties From Mwdmentioning

confidence: 99%

“…However, the use of ML algorithms on MWD data to characterise a rock mass has been demonstrated in other industries. These include tunnelling [51,53,54] and underground mining [52,[57][58][59][60], which employ drilling prior to excavation. Exploration and laboratory-based activities [61][62][63] have also correlated rock mass properties with drilling metrics In a series of classic studies, Schunnesson (1990Schunnesson ( , 1996Schunnesson ( , 1997Schunnesson ( , 1998 drill monitoring was used in underground Scandanavian iron-ore and copper mines to distinguish changing conditions in each investigated rock mass [57][58][59][60].…”

Section: Commoditiesmentioning

confidence: 99%

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A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data

Goldstein,

Aldrich,

O'Connor

2024

Preprint

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Measure While Drilling (MWD) refers to a comprehensive array of sensors employed to gather performance data throughout the process of rock drilling in mining blast holes. Multiple MWD metrics are collected by researchers depending on the setup of MWD sensors. Trends in MWD data can be correlated with pre-excavation subsurface conditions. The low-cost, extensive MWD data acquisition resolution of blast holes compared to expensive and sparsely collected exploration drill holes make the former one a desirable method for characterizing pre-excavation subsurface conditions. Nevertheless, the application of MWD technology in open pit mining is constrained by a substantial amount of data that requiring manual analysis within a short time. To address this limitation, computerized data acquisition and interpretation of MWD variables has been used to broadly determine open pit mining lithological boundaries. The results of artificial intelligence algorithms, such as Neural Networks, and Gaussian Processes, to recognize subsurface conditions, such as rock type, from MWD data have consistently returned &gt;90% prediction accuracy. However, methods of evaluating the importance of each MWD feature, such as Principal Component Analysis, has not been optimally used. This review focuses on the progression of intelligent MWD analysis using Machine Learning methods in open-pit mining, to determine the geotechnical, geological, and geochemical subsurface characteristics before open-pit mining excavation.

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Section: Commoditiesmentioning

confidence: 99%

Section: Rock Mass Properties From Mwdmentioning

confidence: 99%

Section: Commoditiesmentioning

confidence: 99%

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A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data

Goldstein,

Aldrich,

O'Connor

2024

Preprint

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“…The results illustrates the feasibility of the proposed ANNbased design method with much less computing time compared with numerical methods. In a different way, Liu et al [103] explored the correlation between MWD data and support patterns using ANN and found that a neural network with a 6-30-6 topology structure was optimum, whose calculation time was approximately 10 min.…”

Section: Conventionalmentioning

confidence: 99%

Data acquisition-interpretation-aggregation for dynamic design of rock tunnel support

Ling

et al. 2022

Automation in Construction

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“…from the microscopic to macroscopic scale (Lee et al, 2017;Li et al, 2020;Wang et al, 2020;Wu et al, 2020;Yang et al, 2016;Zhou et al, 2014). The flaws can control the mechanical behavior of rock, and have a significant impact on the safety and stability of rock engineering (Liu et al, 2020). Lots of rock engineering practices have shown that rock engineering failures are usually caused by the initiation and propagation of cracks in the rock mass.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation on cracking behavior of granite with intersecting two-flaws: A flat-joint modeling method

Gao

2020

Lat. Am. j. solids struct.

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In practical rock engineering, it is of considerable significance for the study of mechanical behavior of rock containing flaws. The uniaxial compression failure mechanism of granite containing intersecting two-flaws is investigated through the flat-joint modeling approach. The flat-joint model is introduced briefly, and numerical models of intact granite specimens are established. After that, a series of micro-parameters are confirmed by comparison with the laboratory tests of intact granite specimen. Then uniaxial compression simulations are conducted on granites with intersecting two-flaws. The numerically simulated results show good consistency with laboratory tests, including the strength properties, deformation properties, and failure modes. The strength and deformation characteristics of the granites are dependent on the relative position length. Furthermore, the relationship between the crack evolution process and the stress-strain curves for the specimens is studied concerning the relative position length. Finally, the crack propagation mechanism and cracking type of granite specimens are revealed by analyzing the micro-force field.

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Estimation of tunnel support pattern selection using artificial neural network

Cited by 10 publications

References 58 publications

A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data

A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data

Data acquisition-interpretation-aggregation for dynamic design of rock tunnel support

Numerical investigation on cracking behavior of granite with intersecting two-flaws: A flat-joint modeling method

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