• Journal of the Korea Institute of Building Construction
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• v.7 no.3
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• pp.131-137
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• 2007

To predict deep excavation wall movements is important in the urban areas considering the cost and the safety in construction. Failing to estimate deep excavation wall movements in advance causes too many problems in the projects. The purpose of this study is to propose the forecast model of deep excavation wall movements using artificial neural networks. The data of the Deep Excavation Wall Movements which were done form Long research is used of Artificial neural networks training and apply the real construction work measured data to the Artificial neural networks model. Applying the artificial neural networks to forecast the deep excavation wall movements can significantly contribute to identifying and preventing the accident in the overall construction work.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.10a
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• pp.15-26
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• 2005

This paper concerns the assessment of impact of deep excavation on surrounding environment with emphasis on the groundwater lowering. Fundamentals of ground excavation and groundwater interaction were reviewed and the stress-pore pressure coupled analysis approach as a tool for assessment was introduced. A case study concerning the use of coupled analysis for deep excavation design was presented. Implications of the finding from from this study were discussed.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.811-818
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• 2010

This paper presents the results of a numerical investigation on behavior of deep excavation wall system supported by steel pipe struts. A series of three-dimensional finite element analyses were carried out on a deep excavation project site which adopted steel pipe struts. The results indicated that the mechanical behavior of steel pipe supported deep excavation is comparable to that of a conventional H-pile supported deep excavation, although the steel pipe supported system is required less number of struts than the conventional H-pile strut system. Also shown is that the sectional stresses of the steel pipe support system are within the allowable values implying that the steel pipe support system can be effectively used as an alternative to conventional H-pile support system.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.85-95
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• 2002

A substantial portion of the cost of deep excavations in urban environments is devoted to prevent ground movements and their effects on adjacent buildings and utilites. Prediction of ground movements and assessment of the risk of damage to adjacent structures has become an essential part of the planning, design, and construction of a deep excavation project in the urban environments. This paper presents damage assessment techniques for buildings and utilities adjacent deep excavation, which can be readily used in practice.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.451-458
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• 2002

This paper presents the prediction of deep excavation-induced ground surface movements using artificial neural network, which is of prime importance in the perspective of damage assessment of adjacent buildings. A finite element model, which can realistically replicate deep-excavation-induced ground movements was employed and validated against available large-scale model test results. The validated model was then used to perform a parametric study on deep excavations with emphasis on ground movements. Using the result of the finite element analysis, Artificial Neural Network(ANN) system is formed, which can be used in the prediction of deep exacavation-induced ground surface displacements. The developed ANN system can be effecting used for a first-order prediction of ground movements associated with deep-excavation.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.395-406
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• 2008

An excavation with the depth of 32.7m will be constructed as a ventilation shaft in Shanghai metro Line 9. The excavation induced effect on a nearby undercrossing road in operation must be properly evaluated. A centrifuge model test was conducted to study the impact of deep excavation on this existing undercrossing. Detail simulation works are described in this paper. The excavation steps could be simulated in the no-stop state of centrifuge machine. And induced settlements of the undercrossing road in both parallel and vertical directions were analyzed. Protective partition cement soil piles were also simulated in the tests. Simulation test shows deep excavation has a great influence on undercrossing road and the partition pile can obviously deduce the influence.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.77-84
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• 2003

Deep excavations in the urban areas have been frequently going on in large scale. Soil-nailing and Earth-anchor supporting methods are generally used in deep excavation. These construction methods cause ground disturbances during drilling process, and damages of adjacent structures and ground due to the differential settlement throughout construction period, and unexpected behaviors of supporting system according to the characteristics of drilling machine and ground condition. This article introduces two actual examples of adjacent deep excavation for the construction of university buildings in granitic Seoul area. The important results of construction and measurements obtained using Crawler drilling machine for Soil-nailing and Earth-anchor supporting methods are summarized. And some suggestions are given to improve and develop the technique of design and construction in the deep excavation projects having similar ground condition and supporting method.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.69-76
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• 2003

This paper presents the prediction of deep excavation-induced ground surface movements using artifical neural network(ANN) technique, which is of prime importance in the perspective of damage assessment of adjacent buildings. A finite element model, which can realistically replicate deep excavation-induced ground movements was employed to perform a parametric study on deep excavations with emphasis on ground movements. The result of the finite element analysis formed a basis for the Arificial Neural Network(ANN) system development. It was shown that the developed ANN system can be effecting used for a first-order prediction of ground movements associated with deep-excavation.

• Journal of the Korean Geotechnical Society
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• v.20 no.3
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• pp.53-65
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• 2004

This paper presents the prediction of deep excavation-induced ground surface movements using artificial neural network(ANN) technique, which is of prime importance in the damage assessment of adjacent buildings. A finite element model, which can realistically replicate deep excavation-induced ground movements, was employed to perform a parametric study on deep excavations with emphasis on ground movements. The result of the finite element analysis formed a basis for the Artificial Neural Network(ANN) system development. It was shown that the developed ANN system can be effective for a first-order prediction of ground movements associated with deep-excavation.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.1433-1437
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• 2010

The deep excavation work in Korean downtown is almost excuted near by existing structures and utility lines because of the diminution of available yard for construction. So, it was required more and more that the accurate control of displacement on the earth retaining system for minimizing the popular complaint and the damage from constructional accident. Automatic monitoring system is adopted in fracture zone for real time monitoring. In addition, Face mapping is carried out on the face of fracture zone according to excavation sequence. As the result of automatic monitoring system and face mapping, we was able to take the necessary reinforcement and changing excavation method within suitable time. This paper is informed about a stability case on the deep rock excavation site with fracture zone in urban area by automatic monitoring system.