• Geomechanics and Engineering
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• 제14권4호
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• pp.315-324
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• 2018

With rapid economic growth, numerous deep excavation projects for high-rise buildings and subway transportation networks have been constructed in the past two decades. Deep excavations particularly in thick deposits of soft clay may cause excessive ground movements and thus result in potential damage to adjacent buildings and supporting utilities. Extensive plane strain finite element analyses considering small strain effect have been carried out to examine the wall deflections for excavations in soft clay deposits supported by diaphragm walls and bracings. The excavation geometrical parameters, soil strength and stiffness properties, soil unit weight, the strut stiffness and wall stiffness were varied to study the wall deflection behaviour. Based on these results, a multivariate adaptive regression splines model was developed for estimating the maximum wall deflection. Parametric analyses were also performed to investigate the influence of the various design variables on wall deflections.

• 동굴
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• 제67호
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• pp.43-51
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• 2005

본 연구는 수문지형학적 관점에서 고봉동습지를 연구하였다. 고봉산의 남서쪽에 위치한 습지는 산지의 개석과 심층풍화의 영향을 많이 받은 곳이다. 지질적으로는 호상편마암에 속하는 지역이다. 3개 사면으로 부터 지하수 및 지표수를 공급받으며, 습지수원의 대부분은 지하수이다. 습지 지역은 지하수면과 가까이 발달하고 있다. 토양적 조건은 습지 발달에 유리하다. 장석과 운모의 점토광물화로 인하여 습지수의 저류성을 높여주기 때문이다.

• 한국지반공학회논문집
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• 제16권5호
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• pp.55-62
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• 2000

심층혼합처리공법에 의해 개량된 연약지반상에 사석제를 축조한 후 배면매립을 실시할 경우 사석호안의 변형거동을 관찰하기 위해 계측을 실시하였다. 개량지반상에 사석호안을 축조하기 위한 설계기준을 마련하기 위하여 유한요소법을 이용한 지반변형해석과 Bishop의 간편법을 이용한 사면안정해석을 실시하였다. 이들 해석을 사석제부, 연약지반부 그리고 뒷채음성토부의 토질조건 및 형상이 다른 여러 경우에 대하여 실시하여 그 결과를 서로 비교 분석하였다. 즉, 해석결과를 이용하여 사석제 정사부에서의 수평변위량과 침하？을 호안의 사면안정률과 연과지어 검토하였다. 본 연두결과 사석호안의 사면안전률 1.3보다 낮을 경우 상당히 큰 지반변형이 발생하는 것으로 나타났다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 추계 학술발표회 3차
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• pp.113-116
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• 2010

Ground cave-in is usually initiated by the formation of cavity within the ground due to soil loss. When the location of the cavity is deep in the ground, the detection of the cavity is not easy. Then it is possible that the hidden cavity expands for a long time to eventually cause sudden large-scale collapse. A case of large scale ground collapse in the old fill ground was studied and described in this paper. The underground cavity appeared to be caused by subsurface erosion deep in the ground and to expand/extend upward till it was ended by the catastrophic ground failure. It highlighted the importance of proper drainage work in a large scale land fill.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.140-140
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• 2019

본 연구에서는 Terra MODIS 위성자료와 Tensorflow를 활용해 1 km 공간 해상도의 토양수분을 산정하는 알고리즘을 개발하고, 국내 관측 자료를 활용해 검증하고자 한다. 토양수분 모의를 위한 입력 자료는 Terra MODIS NDVI(Normalized Difference Vegetation Index)와 LST(Land Surface Temperature), GPM(Global Precipitation Measurement) 강우 자료를 구축하고, 농촌진흥청에서 제공하는 1:25,000 정밀토양도를 기반으로 모의하였다. 여기서, LST와 GPM의 자료는 기상청의 종관기상관측지점의 LST, 강우 자료와 조건부합성(Conditional Merging, CM) 기법을 적용해 결측치를 보간하였고, 모든 위성 자료의 공간해상도를 1 km로 resampling하여 활용하였다. 토양수분 산정 기술은 인공 신경망(Artificial Neural Network) 모형의 딥 러닝(Deep Learning)을 적용, 기계 학습기반의 패턴학습을 사용하였다. 패턴학습에는 Python 라이브러리인 TensorFlow를 사용하였고 학습 자료로는 농촌진흥청 농업기상정보서비스에서 101개 지점의 토양수분 자료(2014 ~ 2016년)를 활용하고, 모의 결과는 2017 ~ 2018년까지의 자료로 검증하고자 한다.

• Journal of Biosystems Engineering
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• 제33권5호
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• pp.355-361
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• 2008

As agricultural machinery has become larger and tillage practices have changed in recent decades, compaction as a result of wheel traffic and tillage has caused increasing concern. If strategies to manage compaction, such as deep tillage, could be applied only where needed, economic and environmental benefits would result. For such site-specific compaction management to occur, compacted areas within fields must be efficiently sensed and mapped. We previously developed an on-the-go soil strength profile sensor (SSPS) for this purpose. The SSPS measures within-field variability in soil strength at five soil depths up to 50 cm. Determining the variability structure of SSPS data is needed for site-specific field management since the variability structure determines the required intensity of data collection and is related to the delineation of compaction management zones. In this paper, soil bin data were analyzed by a spectral analysis technique to determine the variability structure of the SSPS data, and to investigate causes and implications of this variability. In the soil bin, we observed a repeating pattern due to soil fracture with an approximate 12- to 19-cm period, especially at the 10-cm depth, possibly due to cyclic development of soil fracture on this interval. These findings will facilitate interpretation of soil strength data and enhance application of the SSPS.

• Earthquakes and Structures
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• 제24권5호
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• pp.345-352
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• 2023

This paper studied the effect of soil-structure interaction (SSI) on the seismic response and retrofit of a reinforced concrete structure with a soft-first story for different soil types. A 5-story structure built on a 30m deep homogeneous soil mass was considered as a case study structure, and steel column jacketing and steel bracing were chosen as seismic retrofit methods. Seismic responses of a fixed-base and a flexible base structure subjected to seven scaled earthquake records were obtained using the software OpenSees to investigate the effect of soil on seismic response and retrofit. The nonlinearBeamColumn elements with the fiber sections were used to simulate the nonlinear behavior of the beams and columns. Soil properties were defined based on shear wave velocity according to categorized site classes defined in ASCE-7. The finite element model of the soil was made using isoparametric four-noded quadrilateral elements and the nonlinear dynamic responses of the combined system of soil and structure were calculated in the OpenSees. The analysis results indicate that the soil-structure interaction plays an important role in the seismic performance and retrofit of a structure with a soft-first story. It was observed that column steel jacketing was effective in the retrofit of the model structure on a fixed base, whereas stronger retrofit measures such as steel bracing were needed when soil-structure interaction was considered.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 추계 학술발표회
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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.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2004년도 춘계학술대회 논문집
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• pp.565-569
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• 2004

This paper deals with the characteristics of potential rise and effective impulse impedance of deep-driven ground rods that are used in high resistivity soil or in confined places such as downtown. Also the effects of the impulse and fault currents on the deep-driven ground rods combined with different type grounding electrodes like as mesh grids and counterpoises are described. The $8/20{\mu}s$ impulse current and other wave currents with different rise times are injected into the test ground rod and the effective impedances are examined. The most effective way to obtain the fine transient impedance behaviors of deep-driven ground rods is to reduce the inductive component of grounding electrode systems combined with other ground electrodes.

• 디지털산업정보학회논문지
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• 제15권2호
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• pp.19-28
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• 2019

The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', 'deep learning', and 'artificial neural network' in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.