• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.261-271
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• 2003

A condition evaluation procedure for the pavement foundations using multi-load level Falling Weight Deflectometer(FWD) deflections is presented in this paper. A dynamic finite element program incorporating a stress-dependent material model, was used to generate the synthetic deflection database. Based on this synthetic database, the relationships between surface deflections and critical responses, such as stresses and strains in base and subgrade layers, have been established. FWD deflection data, Dynamic Cone Penetrometer(UP) data, and repeated load resilient modulus testing results used in developing this procedure were collected from the Long Term Pavement Performance (LTPP) and North Carolina Department of Transportation (NCDOT) database. Research effort focused on investigation of the effect of the FWD load level on the condition evaluation procedures. The results indicate that the proposed procedure can estimate the pavement foundation conditions. It is also found that structurally adjusted Base Damage Index (BDI) and Base Curvature Index (BCI) are good indicators for the prediction of stiffness characteristics of aggregate base and subgrade respectively. A FWD test with a load of 66.7 kN or less does not improve the accuracy of this procedure. Results from the study for the nonlinear behavior of a pavement foundations indicate that the deflection ratio obtained from multi-load level deflections can predict the type and quality of the pavement foundation materials.

• Structural Engineering and Mechanics
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• v.39 no.5
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• pp.621-642
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• 2011

Passive tuned mass dampers (TMDs) efficiently suppress vibrations induced by quasi-stationary dynamic inputs, such as winds, sea waves or traffic loads, but may prove of little use against pulse-like excitations, such as near-field (NF) ground motions. The extent of such impairment is however controversial, partly due to the different evaluation criteria adopted within the literature, partly to the limited number of seismic records used in most investigations. In this study, three classical techniques and two new variants for designing a TMD on an SDOF structure are tested under 338 NF records from the PEER NGA database, including 156 records with forward-directivity features. Percentile response reduction spectra are introduced to statistically assess TMD performance, and TMD robustness is verified through Monte Carlo simulations. The methodology is extended to a variety of MDOF bending-type and shear-type frames, and simulated on a case study building structure recently constructed in Central Italy.Results offer an interesting insight into the performance of TMDs against NF earthquakes, ultimately showing that, if properly designed and sufficiently massive, TMDs are effective and robust even in the face of pulse-like ground motions. The two newly proposed design techniques are shown to generally outperform the classical ones.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.441-446
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• 2005

막대한 비용을 들여 모든 건설공사에서 시행하는 지반조사의 결과물을 현재는 종이문서 형태로 각 관리주체가 개별적으로 보관하고 있어 재활용과 공유가 거의 이루어지고 있지 않은 실정이다. 이를 데이터베이스화하여 웹을 통해 서비스함으로써 효과적인 저장과 활용을 유도할 수 있다. 그 일환으로 현재 웹 기반의 지반정보 통합 D/B 시스템을 구축, 운영 중에 있다. 현재는 지방국토관리청이나 공사현장과 같이 지반정보가 발생하는 기관 및 현장을 직접 방문하여 지반조사 결과물을 수집한 후 이를 전산화와 검수를 실시해 시스템에 반영을 하는 방식으로 지반정보를 데이터베이스화하고 있지만, 많은 시간과 비용이 소요되고 수집에 한계가 있어 이를 개선하기 위한 자동화시스템의 도입이 검토되고 있다. 지반정보의 수집, 전산화, 검수, 웹서비스의 일련 과정을 보다 자동화시키고, 효과적이며, 수집범위를 획기적으로 넓힐 수 있는 방안으로 웹 기반의 입력자동화 시스템 도입, 지반정보 관리 주체의 직접 입력 지침 마련, 입력 실무자에 대한 권한 부여와 사용자 교육 등에 대한 전략을 본 연구를 통해 모색해 보았다.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.10a
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• pp.185-192
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• 1999

This paper presents the results of a parametric study on the behavior of tunnel face reinforced with horizontal pipes. A three-dimensional finite element model was adopted in this study to capture the three-dimensional nature of tunnel face behavior under various boundary conditions. A parametric study was peformed on a wide range of boundary conditions with emphasis on the effect of reinforcing layouts on the deformation behavior of tunnel face. The results of analysis such as tunnel face deformation behavior under various conditions were thoroughly analyzed, and a database for the behavior of tunnel face under different reinforcing conditions was established for future development of a semi-empirical design/analysis method for the tunnel face reinforcing technique. The results indicated that there exits an optimum reinforcing layout for a given tunnel condition, which must be selected with due consideration of tunnel geometry and ground condition.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.101-108
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• 2000

In this study, an expert system was developed to predict the safety of tunnel and choose proper tunnel reinforcement system using fuzzy quantification theory and fuzzy inference rule based on tunnel information database. The expert system developed in this study have two main parts named pre-module and post-module. Pre-module decides tunnel information imput items based on the tunnel face mapping information which can be easily obtained in-situ site. Then, using fuzzy quantification theory II, fuzzy membership function is composed and tunnel safety level is inferred through this membership function. The comparison result between the predicted reinforcement system level and measured ones was very similar. In-situ data were obtained in three tunnel sites including subway tunnel under Han river, This system will be very helpful to make the most of in-situ data and suggest proper applicability of tunnel reinforcement system developing more resonable tunnel support method from dependance of some experienced experts for the absent of guide.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.705-707
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• 2005

For the development of geothermal energy, many different kind of geoscientific data including both surface geological data and underground geomechanical data, are acquired. Integration of such data itself for better understanding of underground condition is not a simple process due to complexity of the data, i.e. mixture of 20 and 3D data, mixture of geological data, geochemical data, geomechanical data and hydrogeological data. This paper reports a preliminary suggestion of GIS modeling for such specific purpose. Data used for GIS modeling mainly came from British case studies. The modeling is much more focused on the design of database for 3D underground geotechnical data in this study.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.673-680
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• 2000

Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this study, a back-propagation neural network model for estimating a consolidation behavior of clay from soil parameter, site investigation data and the first settlement curve is proposed. The training and testing of the network were based on a database of 63 settlement curve from two different sites. Five different network models were used to study the ability of the neural network to predict the desired output to increasing degree of accuracy. The study showed that the neural network model predicted a consolidation behavior of clay reasonably well.

• Journal of the Korean Society for Railway
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• v.5 no.2
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• pp.84-92
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• 2002

For an efficient management and analysis of geological/geotechnical data obtained during site investigations or tunnel construction, Tunnel Information System(TIS) was developed in this study. TIS is running in CIS(Geographical Information System) which has a spatial data. TIS consists of two parts, the Tunnel Face Mapping System(FaceMap), to record a geological features by observations and measurements at the surface of the excavation, the Borehole Data Management System(BDMS), to store the different types of rock data related to boreholes. Using the database of collapsed tunnels, 20 in Korea and 84 in Europe and with an artificial neural network, an expert system was developed for inferring the tunnel collapse pattern and its volume. And by applying Geo-predict, the system developed, in tunnels under construction, observed data from the $\bigcirc$$\bigcirc$tunnl site was compared and analyzed.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.759-766
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• 2002

In the paper the authors describe the development of ITIS(Intelligent Tunneling Information System) for the Purpose of applying the 3D visualization technique, GIS, AI(Artificial Intelligence) to tunnel design and construction. VR(Virtual Reality) and 3D visualization techniques are applied in order to develope the 3D model of characteristics and structures of ground and rock mass. Database for all the materials related to site investigation and tunnel construction is developed using GIS technique. AI technique such as fuzzy theory and neural network is applied to predict ground settlement, decide tunnel support method and estimate ground and rock mass properties according to tunnel excavation steps. ITIS can help to inform various necessary tunnel information to engineers quickly and manage tunnel using acquired information based on D/B.

• Journal of the Korean Geotechnical Society
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• v.21 no.8
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• pp.73-84
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• 2005

A three layered neural network model was developed using back propagation algorithm to estimate the UU undrained shear strength of Korean soft soil based on the database of actual undrained shear strengths and piezocone measurements compiled from 8 sites over the Korea. The developed model was validated by comparing model predictions with measured values about new piezocone data, which were not previously employed during development of model. Performance of the neural network model was also compared with conventional empirical methods. It was found that the number of neuron in hidden layer is different for the different combination of transfer functions of neural network models. However, all piezocone neural network models are successful in inferring a complex relationship between piezocone measurements and the undrained shear strength of Korean soft soils, which give relatively high coefficients of determination ranging from 0.69 to 0.72. Since neural network model has been generalized by self-learning from database of piezocone measurements and undrained shear strength over the various sites, the developed neural network models give more precise and generally reliable undrained shear strengths than empirical approaches which still need site specific calibration.