• 한국가시화정보학회:학술대회논문집
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• 2005.12a
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• pp.93-98
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• 2005

The mean and RMS velocity field of the respiratory gas flow in tile human airway was studied experimentally by particle image velocimetry(PIV). Some researchers investigated the airflow for the mouth breathing case both experimentally and numerically. But it is very rare to investigate the airflow of nose breathing in a whole airway due to its geometric complexity. We established the procedure to create a transparent rectangular box containing a model of the human airway for PIV measurement by combination of the RP and the curing of clear silicone. We extend this to make a whole airway including nasal cavities, larynx, trachea, and 2 generations of bronchi. The CBC algorithm with window offset (64*64 to 32*32) is used for vector searching in PIV analysis. The phase averaged mean and RMS velocity distributions in Sagittal and coronal planes are obtained for 7 phases in a respiratory period. Some physiologic conjectures are obtained. The main stream went through the backside of larynx and trachea in inspiration and the frontal side in expiration. There exist vortical motions in inspiration, but no prominent one in expiration.

• Tunnel and Underground Space
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• v.25 no.4
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• pp.373-382
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• 2015

This study presents a method to calculate carbon dioxide emissions of diesel vehicles operated in an underground mine using Geographic Information Systems (GIS). An underground limestone mine in Korea was selected as the study area. A GIS database was constructed to represent the haulage roads as a 3D vector network. The speed of dump trucks at each haulage road was investigated to determine the carbon dioxide emission factor. The amount of carbon dioxide emissions related to the truck's haulage work could be calculated by considering the carbon dioxide emission factor at each haulage road and the haulage distance determined by GIS-based optimal route analysis. Because diesel vehicles are widely utilized in the mining industry, the method proposed in this study can be used and further improved to calculate the amount of carbon dioxide emissions in mining sites.

• Structural Engineering and Mechanics
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• v.28 no.2
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• pp.129-152
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• 2008

This paper considers the solution of the stochastic differential equations (SDEs) with random operator and/or random excitation using the spectral SFEM. The random system parameters (involved in the operator) and the random excitations are modeled as second order stochastic processes defined only by their means and covariance functions. All random fields dealt with in this paper are continuous and do not have known explicit forms dependent on the spatial dimension. This fact makes the usage of the finite element (FE) analysis be difficult. Relying on the spectral properties of the covariance function, the Karhunen-Loeve expansion is used to represent these processes to overcome this difficulty. Then, a spectral approximation for the stochastic response (solution) of the SDE is obtained based on the implementation of the concept of generalized inverse defined by the Neumann expansion. This leads to an explicit expression for the solution process as a multivariate polynomial functional of a set of uncorrelated random variables that enables us to compute the statistical moments of the solution vector. To check the validity of this method, two applications are introduced which are, randomly loaded simply supported reinforced concrete beam and reinforced concrete cantilever beam with random bending rigidity. Finally, a more general application, randomly loaded simply supported reinforced concrete beam with random bending rigidity, is presented to illustrate the method.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Structural Engineering and Mechanics
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• v.53 no.2
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• pp.205-226
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• 2015

Finite element method (FEM) is an effective quantitative method to solve complex engineering problems. The basic idea of FEM for a complex problem is to be able to find a solution by reducing the problem made simple. If mathematical tools are inadequate to obtain precise result, even approximate result, FEM is the only method that can be used for structural analyses. In FEM, the domain is divided into a large number of simple, small and interconnected sub-regions called finite elements. FEM has been used commonly for linear and nonlinear analyses of different types of structures to give us accurate results of plane stress and plane strain problems in civil engineering area. In this paper, FEM is used to investigate stress analysis of a shear wall which is subjected to concentrated loads and fundamental principles of stress analysis of the shear wall are presented by using matrix displacement method in this paper. This study is consisting of two parts. In the first part, the shear wall is discretized with constant strain triangular finite elements and stiffness matrix and load vector which is attained from external effects are calculated for each of finite elements using matrix displacement method. As to second part of the study, finite element analysis of the shear wall is made by ANSYS software program. Results obtained in the second part are presented with tables and graphics, also results of each part is compared with each other, so the performance of the matrix displacement method is demonstrated. The solutions obtained by using the proposed method show excellent agreements with the results of ANSYS. The results show that this method is effective and preferable for the stress analysis of shell structures. Further studies should be carried out to be able to prove the efficiency of the matrix displacement method on the solution of plane stress problems using different types of structures.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.1
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• pp.23-34
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• 2020

This paper covers the development of prediction techniques for ice load on ice-breakers operating in continuous ice-breaking under level ice conditions using particle-based continuum mechanics. Ice is assumed to be a linear elastic material until the fracture occurs. The maximum normal stress theory is used for the criterion of fracture. The location of the crack can be expressed using a local scalar function consisting of the gradient of the first principal stress and the corresponding eigen-vector. This expression is used to determine the relative position of particle pair to the new crack. The Hertz contact model is introduced to consider the collisions between ice fragments and the collisions between hull and ice fragments. In order to verify the developed technique, the simulation results for the three-point bending problems of ice-specimen and the continuous ice-breaking problem around a wedge-type model ship with bow angle of 20° are compared with the experimental results carrying out at Korea Research Institute of Ships and Ocean Engineering (KRISO).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2A
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• pp.383-390
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• 2006

In this paper, a stochastic field that is compatible with Monte Carlo simulation is suggested for an expansion-based stochastic analysis scheme of weighted integral method. Through investigation on the way of affection of stochastic field function on the displacement vector in the series expansion scheme, it is noticed that the stochastic field adopted in the weighted integral method is not compatible with that appears in the Monte Carlo simulation. As generally recognized in the field of stochastic mechanics, the response variability is not a linear function of the coefficient of variation of stochastic field but a nonlinear function with increasing variability as the intensity of uncertainty is increased. Employing the stochastic field suggested in this study, the response variability evaluated by means of the weighted integral scheme is reproduced with high precision even for uncertain fields with moderately large coefficient of variation. Besides, despite the fact that only the first-order expansion is employed, an outstanding agreement between the results of expansion-based weighted integral method and Monte Carlo simulation is achieved.

• Steel and Composite Structures
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• v.51 no.6
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

• Tunnel and Underground Space
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• v.13 no.3
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• pp.187-195
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• 2003

This case study which is to make 2-Dimension earth's crust structures clearly is about the great boring-hole blasting to provide ground vibration source of the reflected wave research on the Korean Peninsula earth's crust structures research. For this study we've done blasting twice-500 ㎏/charge per delay, 1,000 ㎏/charge per delay, and the specifications of blasting are the following - dia.: 300 ㎜, boring-depth : 100m, besides, we used the explosives and electric detonators which have sufficient detonating velocity and very excellent safety, capacity of detonating, accurate delay time. We charged explosives into steel pipe with bulk type to avoid dead pressure by ground water. And then we tested about pipe airtight and blasting to certificate which has no problem by using on this study. In the results, we succeeded each blasting in Seosan, Youngdong. For the Peak Sum Vector(PSV) around the blasting at the main points, its real measured PSV is higher 180 % than estimated PSV with USBM. In this study we can't to be analysis of vibration velocity, but to be key providing vibration source.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.28-38
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• 2005

According to the recent development of measurement system utilizing one or a set of boreholes, visualization of the explored underground became to be a major issue. It induced even the introduction of monitoring apparatuses on the borehole wall with multi-function tool, but the usage of these was often limited by where is unfavorable rock condition and a few of engineers can approach. And so, a portable type of borehole camera with only the essential function has been investigated and a few of commercial models about this is recently being applied into the field condition. This paper was based on the monitoring results obtained using a commercial model by Dr. Nakagawa. Discontinuities in rock mass were the topic for the visualization, and it was studied how can visualize their three dimensional distribution and what a numerical formulation is needed and how to understand the visualization result. The numerical formulation was based on the geometric correlation between the dip direction / dip of discontinuous plane and the trend / plunge of borehole, a set of the equation of a plane was induced. As field application of this into two places, it is found that the above visualization methodology will be especially an useful geotechlical tool for analyzing the local distribution of discontinuities.