• Journal of the Korean Geotechnical Society
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• v.24 no.1
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• pp.61-70
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• 2008

The control and prediction of surface settlement, gradient and ground displacement are the main factors in shallow tunnel design and construction in urban area. For deformation analysis of shallow tunnel due to excavation it is important to identify possible deformation mechanism of shear bands developing from tunnel shoulder to the ground surface. This paper investigaties quantitatively the deformation behavior of shallow tunneling by model tunnel test and strain softening analysis Incorporating the reduction of shear stiffness and strength parameters. The comparison of model tunnel test result and numerical simulation using strain softening analysis showed good agreement in crown settlement, normalized subsidence settlement and developing shear bands above tunnel shoulder. In this study, it is blown that the strain softening modeling is applicable to the nonlinear deformation analysis of shallow tunnel.

• Composites Research
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• v.19 no.1
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• pp.1-8
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• 2006

Prediction of damage caused by low-velocity impact in laminated composite plate is an important problem faced by designers using composites. Not only the inplane stresses but also the interlaminar normal and shear stresses playa role in estimating the damage caused. But it is well known that the conventional approach based on the homogenization has the limit in description of damage. The work reported here is an effort in getting better predictions of dynamic behavior and damage in composite plate using DNS approach. In the DNS model, we discretize the composite plates through separate modeling of fiber and matrix for the local microscopic analysis. In the view of microscopic mechanics with DNS model, interlaminar stress behaviors in the inside of composite materials are investigated and compared with the results of the homogenized model which has been used in the conventional approach to impact analysis. Also the multiscale model based on DNS concept is developed in order to enhance the effectiveness of impact analysis, and we present the results of multiscale analysis considering micro and macro structures simultaneously.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.287-295
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• 2023

The collapse of bridges due to earthquakes results in many casualties and property damages. Thus, accurate prediction and preparation are required for the behavior of bridges during earthquakes. In particular, columns play an important role in the seismic behavior of bridges. The risk of collapse due to an earthquake increases when there is a problem of the insufficient lap splice in the column. In this study, to analyze the characteristics of the lap splice in the column, a numerical model was defined for the insufficient lap-spliced columns and verified using experimental data. The developed column model was applied to a commonly used RC slab bridge. Nonlinear static analysis for the column was performed to evaluate the change in the performance of the column according to the lap-spliced length. In addition, this study assessed the effect of the lap-spliced length on the seismic fragility analysis.

• Earthquakes and Structures
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• v.22 no.5
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• pp.503-515
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• 2022

This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategies.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.63-63
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• 2020

최근 머신러닝 기술의 발전에 따라 비선형 시계열자료에 대한 예측이 가능해졌으며, 기존의 과정기반모형을 대체하여 지하수, 하천수 예측 등 다양한 수문분야에 활용되고 있다. 본 연구에서는 기존의 연구들과 달리 과정기반모형을 이용한 하천수 모의결과를 개선하기 위해 과정기반모형과 결합하는 방식으로 머신러닝 기술을 활용하였다. 머신러닝 기술을 통해 관측값과 모의값 간의 차이를 예측하고 과정기반모형의 모의결과에 반영함으로써 관측값을 정확히 재현할 수 있도록 하는 시스템을 구축하고 평가하였다. 과정기반모형으로는 Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro)을 소양강 유역을 대상으로 구축하였다. 머신러닝 모형으로는 순환 신경망 중 하나인 Long Short-Term Memory (LSTM) 신경망을 이용하여 장기시계열예측이 가능하게 하였다(WRF-Hydro-LSTM). 머신러닝 모형은 2013년부터 2017년까지의 기상자료 및 유입량 잔차를 이용하여 학습시키고, 2018년 기상자료를 이용하여 예상되는 유입량 잔차를 모의하였다. 모의된 잔차를 WRF-Hydro 모의결과에 반영시켜 최종 유입량 모의값을 보정하였다. 또한, 연구에서 제안된 새로운 방법론의 성능을 비교평가하기 위해 머신러닝 단독 모형으로 유입량을 학습 후 모의하였다(LSTM-only). 상관계수와 Nash-Sutcliffe 효율계수(NSE)를 사용해 평가한 결과, LSTM을 이용한 두 방법(WRF-Hydro-LSTM과 LSTM-only) 모두 기존의 과정기반모형(WRF-Hydro-only)에 비해 높은 정확도의 하천수 모의가 가능했으며, PBIAS 지수를 사용하여 평가한 결과, LSTM을 단독으로 사용하였을 때보다 WRF-Hydro와 결합했을 때 더 관측값과 가까운 모의가 가능함을 확인할 수 있었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6A
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• pp.819-825
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• 2008

This paper describes the shaping formation and the erection of SCST structure by cable-tensioning. It could be a fast and economical method for constructing the space structure consisted with uniform pyramids by cable-tensioning of the cable in bottom chords. In the initial layout, the top chords and web members are left at their true length, the bottom chords are given gaps in proportion to the desired final shape. The feasibility of the proposed shaping method and the reliability of the established geometric model were confirmed with nonlinear finite element analysis and an experimental investigation on small scale and full size test models. As a result, the behaviour characteristic of MERO joint is very significant in shaping analysis of space structure. This study suggests the most reasonable modeling technique for the prediction of shaping in practices. And it is shown the characteristic of the behavior in shaping test for practical design purposes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4C
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• pp.229-238
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• 2006

Observed field behaviors are frequently different from the behaviors predicted in the design state due to several uncertainties involved in soil properties, numerical modeling, and error of measuring system even though a sophisticated numerical analysis technique is applied to solve the consolidation behavior of drainage-installed soft deposits. In this study, genetic algorithms are applied to back-analyze the soil properties using the observed behavior of soft clay deposit composed of multi layers that shows complex consolidation characteristics. Utilizing the program, one might be able to appropriately predict the subsequent consolidation behavior from the measured data in an early stage of consolidation of multi layered soft deposits. Example analyses for drainage-installed multi-layered soft deposits are performed to examine the applicability of proposed back-analysis method.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.81-81
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• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.550-554
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• 2023

Fossil energy sources are limited in their sustainable use and expansion due to global warming caused by carbon dioxide emissions. Hydrogen is considered as a promising alternative to traditional fossil fuels. To ensure the stable long-term storage, it is necessary to accurately predict its thermodynamic properties at cryogenic temperatures. Therefore, this study aimed to investigate thermodynamic properties, such as saturated vapor pressure and density, enthalpy, and entropy of liquid and gas, using cubic equations of state that demonstrate relatively simple relationships. Among the three types of equations of state (Redlich-Kwong (RK), Soave-Redlich-Kwong (SRK), and Peng-Robinson (PR)), the SRK model exhibited relatively accurate prediction results for various physical properties.