• Journal of Ocean Engineering and Technology
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• v.16 no.6
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• pp.7-17
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• 2002

Recently, artificial rocks, instead of buoys, have been placed on the submerged breakwater to indicate its location. The accurate estimation of wave forces on these rocks is deemed necessary for their stability design. Characteristics of the wave force, however, are expected . to be very complicated because of the occurrence of breaking or post-breaking waves. In this regard, wave forces exerted on an artificial rock have been investigated in this paper. The maximum wave force has been found to strongly dependent on the location and shape of the artificial rock that is placed on the submerged breakwater. The plunging breaker occurs near the loading cram edge of a submerged breakwater, which cause impulsive breaking wave force on the rock. Using the Morison equation, with the velocity and acceleration at the front face of the artificial rock and varying water surface level, it is possible to estimate wave forces, even impulsive breaking wave forces, that are acting on the rock installed on a submerged breakwater. The vertical wave force is also found to depend, significantly, on the buoyant force.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.5
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• pp.43-47
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• 2012

A evaluation for the strength of rock includes a lot of uncertainty due to existence of discontinuity surface and weakness plain in the rock mass, so essential test results and other data for the resonable strength analysis are absolutely insufficient. Therefore, a analytical technique to reduce such uncertainty can be required. A probabilistic analysis technique has mainly to make up for the uncertainty to investigate the strength of rock mass. Recently, a artificial neural networks, as a more newly analysis method to solve several problems in the existing analysis methodology, trends to apply to study on the rock strength. In this study the unconfined compressive strength from basic physical property values of sedimentary rock, black shale and red shale, distributed in Daegu metropolitan area is estimated, using the artificial neural networks. And the applicability of the analysis method is investigated. From the results, it is confirmed that the unconfined compressive strength of the sedimentary rock can be easily and efficiently predicted by the analysis technique with the artificial neural networks.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.557-560
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• 2003

Some well-known artificial weathering tests such as freezing-thawing, acid immersion, and salt crystallization are adopted to examine the change of rock properties during the processes of artificial weathering. Granites and other rock types of limestone, marble and basalt collected from different quarries in south Korea were sampled for this study. All tests were performed up to 30 cycles and physical properties were measured after experiencing every ten cycles of artificial weathering tests. During the tests, the variation trends of rock properties were too variable to draw generalized variation patterns but it can be concluded that weathering agents have different effect on rock properties depending on weathering circumstance and time. Even in short terms of salt crystallization tests, some rocks were severely deformed and then burst, and in the early stages of salt weathering, recrystallized salts filling pores and cracks in rocks could be a important factor affecting rock properties.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.353-364
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• 2022

This study presents a hybrid algorithm for classifying the rock joints, where the improved artificial bee colony (IABC) and the fuzzy C-means (FCM) clustering algorithms are incorporated to take advantage of the artificial bee colony (ABC) algorithm by tuning the FCM clustering algorithm to obtain the more reasonable and stable result. A coefficient is proposed to reduce the amount of blind random searches and speed up convergence, thus achieving the goals of optimizing and improving the ABC algorithm. The results from the IABC algorithm are used as initial parameters in FCM to avoid falling to the local optimum in the local search, thus obtaining stable classifying results. Two validity indices are adopted to verify the rationality and practicability of the IABC-FCM algorithm in classifying the rock joints, and the optimal amount of joint sets is obtained based on the two validity indices. Two illustrative examples, i.e., the simulated rock joints data and the field-survey rock joints data, are used in the verification to check the feasibility and practicability in rock engineering for the proposed algorithm. The results show that the IABC-FCM algorithm could be applicable in classifying the rock joint sets.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.127-132
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• 2012

In this study, ambient temperature curing artificial aggregate were developed by using crushed-stone sludge. In order to evaluate the mechanical properties, the artificial aggregate was tested on 7 items. Test results showed that the artificial aggregate mostly satisfied the basic requirements of normal aggregate. The concrete with the artificial aggregate made by weathered rock and granite sludge was tested on the compressive test and flexural test. From the test results, It is confirmed that the concrete with the granite artificial aggregate develope the higher compressive strength than the crushed rock aggregate and the concrete with artificial aggregate concrete have the lower elastic modulus and flexural strength than the concrete with crushed rock aggregate.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 1996.03a
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• pp.35-42
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• 1996

지하 구조물의 안정성 확보와 경제적인 시공을 위하여 상세하고 합리적인 암반분류가 필요하다. 설계 초기에는 제한적인 정보와 암반의 불확실성에 따라 암반분류의 신뢰도가 떨어진다. 이러한 불확실한 지질 정보를 근사하게 추론할 수 있는 방법으로서 인공지능(Artificial intelligence) 특히 인공신경망 (Artificial neural network)이 있다. (중략)

• Tunnel and Underground Space
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• v.31 no.1
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• pp.25-40
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• 2021

As the era of Industry 4.0 arrives, the researches using artificial intelligence in the field of rock engineering as well have increased. For a better understanding and availability of AI, this paper analyzed the types of algorithms and how to apply them to the research papers where AI is applied among domestic and international studies related to tunnels, blasting and mines that are major objects in which rock engineering techniques are applied. The analysis results show that the main specific fields in which AI is applied are rock mass classification and prediction of TBM advance rate as well as geological condition ahead of TBM in a tunnel field, prediction of fragmentation and flyrock in a blasting field, and the evaluation of subsidence risk in abandoned mines. Of various AI algorithms, an artificial neural network is overwhelmingly applied among investigated fields. To enhance the credibility and accuracy of a study result, an accurate and thorough understanding on AI algorithms that a researcher wants to use is essential, and it is expected that to solve various problems in the rock engineering fields which have difficulty in approaching or analyzing at present, research ideas using not only machine learning but also deep learning such as CNN or RNN will increase.

• Tunnel and Underground Space
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• v.3 no.1
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• pp.63-79
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• 1993

The design of tunnels in rock masses often demands more informations on geologic features and rock mass properties than acquired by usual field survey and laboratory testings. In practice, the situation that a perfect set of geological and mechanical input data is given to geomechanics design engineer is rare, while the engineers are asked to achieve a high level of reliability in their design products. This study presents an artificial neural network which is developed to resolve the difficulties encountered in conventional design techniques, particulary the problem of deteriorating the confidence of existing numerical techniques such as the finite element, boundary element and distinct element methods due to the incomplete adn vague input data. The neural network has inferring capabilities to identify the possible failure modes, support requirements and its timing for underground openings, from previous case histories. Use of the neural network has resulted in a better estimate of the correlation between systems of rock mass classifications such as the RMR and Q systems. A back propagation learning algorithm together with a multi-layer network structure is adopted to enhance the inferential accuracy and efficiency of the neural network. A series of experiments comparing the results of the neural network with the actual field observations are performed to demonstrate the abilities of the artificial neural network as a new tunnel design assistance system.

• Geomechanics and Engineering
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• v.12 no.4
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• pp.723-738
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• 2017

Rock is a heterogeneous material, which introduces complexity in the analysis of rock slopes, since both the existing discontinuities within the rock mass and the intact rock contribute to the degradation of strength. Rock failure is often catastrophic due to the brittle nature of the material, involving the sliding along structural planes and the fracturing of rock bridge. This paper proposes an advanced discretization method of rock mass based on block theory. An in-house software, GeoSMA-3D, has been developed to generate the discrete fracture network (DFN) model, considering both measured and artificial joints. Measured joints are obtained from the photogrammetry analysis on the excavation face. Statistical tools then facilitate to derive artificial joints within the rock mass. Key blocks are searched to provide guidance on potential reinforcement measures. The discretized blocky system is subsequently implemented into a discontinuous deformation analysis (DDA) code. Strength reduction technique is employed to analyze the stability of the slope, where the factor of safety can be obtained once excessive deformation of slope profile is observed. The combined analysis approach also provides the failure mode, which can be used to guide the choice of strengthening strategy if needed. Finally, an illustrated example is presented for the analysis of a rock slope of 20 m height inclined at $60^{\circ}$ using combined GeoSMA-3D and DDA calculation.

• Geomechanics and Engineering
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• v.17 no.6
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• pp.597-606
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• 2019

Ultrasonic Time-of-Flight Diffraction (TOFD) techniques are useful methods for non-destructive evaluation of fracture characteristics. This study focuses on the reliability and accuracy of ultrasonic diffraction methods to estimate the depth of rock fractures. The study material includes three different rock types; andesite, basalt and ignimbrite. Four different ultrasonic techniques were performed on these intact rocks. Artificial near-surface fracture depths were created in the laboratory by sawing. The reliability and accuracy of each technique was assessed by comparison of the repeated measurements at different path lengths along the rock surface. The standard error associated with the predictive equations is very small and their reliability and accuracy seem to be high enough to be utilized in estimating the depth of rock fractures. The performances of these techniques were re-evaluated after filling the artificial fractures with another material to simulate natural infills.