• Journal of the Korean Geotechnical Society
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• v.39 no.2
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• pp.5-17
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• 2023

In this study, a classification and uniaxial compression test of soil was conducted on 15 collapsed sites to use ground improvement with excellent protection effect owing to the increase of localized heavy rain in Korea. The Casagrande method and fall cone test were performed on the field soil to derive an expression for comparing liquid limit and plastic limit values, soil classification, and correlation between each other. By deriving the optimal mixing ratio of the ground improvement agent using uniaxial compressive strength for each soil classification, the classification of the fine-grained soil was not clear owing to the proficiency difference and test error. However, after classifying using the fall cone test, it was possible to suggest a clear optimal mixing ratio.

• Geotechnical Engineering
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• v.14 no.1
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• pp.29-36
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• 1998

The application of a multiple rock classification method, which is a generalization of a binary rock classification, is studied in this paper. In particular, this paper shows how to incorporate qualitative data through a case study. The method suggested in this paper can be effectively used for a systematic multiple rock classification such as RMR system developed by Bieniawski. It will be very useful for rock classifications. In addition, it is known that the expected cost of errors can be atopted to indicate how well a investigation plan is made.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.547-560
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• 2023

Recently, research on predicting ground classification using machine learning techniques, TBM excavation data, and ground data is increasing. In this study, a multi-classification prediction study for uniaxial compressive strength (UCS) was conducted by applying random forest model based on a decision tree among machine learning techniques widely used in various fields to machine data and ground data acquired at three slurry shield TBM sites. For the classification prediction, the training and test data were divided into 7:3, and a grid search including 5-fold cross-validation was used to select the optimal parameter. As a result of classification learning for UCS using a random forest, the accuracy of the multi-classification prediction model was found to be high at both 0.983 and 0.982 in the training set and the test set, respectively. However, due to the imbalance in data distribution between classes, the recall was evaluated low in class 4. It is judged that additional research is needed to increase the amount of measured data of UCS acquired in various sites.

• Geotechnical Engineering
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• v.11 no.2
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• pp.71-78
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• 1995

In this paper, a study was performed on classifying a rock mass into multiple classes as in rock mass classification systems, such as RMR system and Q system etc. In a situation with only limited quantitative data available, it was sought to employ a way of incorporating qualitative data in a systematical and reasonable manner. It is based on the realm of Geostatistics. In particular, indicator kriging technique, which is one of non-parametric approaches, was used. As a selection criterion for an optimal classification, the cost of errors was adopted. As a result, the binary rock classification method developed before was extended and generalized for multiple rock classification with its total number of classes unlimited.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.10a
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• pp.185-198
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• 2001

암반 불연속면에 대한 조사는 국제 암반역학회(ISRM)에서 추천하는 조사법이 합리적으로 불연속면이 특성을 기재할 수 있는 방안이기 때문에 널리 적용되고 있다 그러나, 이 안에서는 조사결과를 공학적 의미가 있는 암반특성치로 변환하는 방안에 대한 언급이 없다. 본고에서는 지질기술자들이 불연속면조사시 주로 사용하는 ISRM 추천안인 제시한 조사항목에 토목기술자들이 주로 사용하는 RMR 혹은 Q-system 분류안의 정량적인 값을 적용할 수 있을 지를 검토하였다. 이에 대한 관련기술자들의 관심이 모여질 때 정량적인 인자에 기초한 ISRM 조사법에 부합되는 암반분류안의 구축도 가능하리라 생각된다.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.494-507
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• 2021

With the increasing use of TBM, research has recently been conducted in Korea to analyze TBM data with machine learning techniques to predict the ground in front of TBM, predict the exchange cycle of disk cutters, and predict the advance rate of TBM. In this study, classification prediction of rock characteristics of slurry shield TBM sites was made by combining traditional rock classification techniques and machine learning techniques widely used in various fields with machine data during TBM excavation. The items of rock characteristic classification criteria were set as RQD, uniaxial compression strength, and elastic wave speed, and the rock conditions for each item were classified into three classes: class 0 (good), 1 (normal), and 2 (poor), and machine learning was performed on six class algorithms. As a result, the ensemble model showed good performance, and the LigthtGBM model, which showed excellent results in learning speed as well as learning performance, was found to be optimal in the target site ground. Using the classification model for the three rock characteristics set in this study, it is believed that it will be possible to provide rock conditions for sections where ground information is not provided, which will help during excavation work.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.10c
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• pp.89-102
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• 2001

• Proceedings of the Korean Geotechical Society Conference
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• 1996.10a
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• pp.187-194
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• 1996

• 건축구조
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• v.12 no.5
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• pp.37-40
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• 2005

• Journal of the Earthquake Engineering Society of Korea
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• v.4 no.3
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• pp.55-65
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• 2000

구조물 지진해석을 위한 구조물 -지반 상호작용 해석에서도 비선형 지반 특성을 고려한 비선형해석이 요구되고 있어 구조물 비선형 지진 해석을 위해 기초 지반에 대한 수평방향 비선형 해석을 수행하였다. 기초지반은 UBC 분류에서 규정한 보통지반인 Sn 지반과 연약지반인 SE 지반을 고려하였고, 지반의 비선형 특성은 Ramberg-Osgood 모델을 이용하였다. 비선형 지반이 기초지반 수평 및 회전 동적 강성 및 감쇠비에 미치는 영향을 조사하기 위하여 얕은 기초와 묻힌기초에 대해 기초 크기, 지반깊이 및 말뚝유무에 따른 동적 강성 및 감쇠비 변화를 조사하였는데, 지반의 비선형 특성이 기초지반의 선형 수평 및 회전 강성과 감쇠비를 크게 감소 또는 증가시키는 것으로 나타났으며, 기초크기, 지반깊이 및 말뚝유무의 영향도 큰 것으로 나타나 구조물 지진해석시 기초크기, 지반깊이 및 말뚝유무와 함께 지반의 비선형성도 고려하는 것이 필요한 것으로 판단되었다.