• 한국환경복원기술학회지
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• 제10권5호
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• pp.76-86
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• 2007

For the purpose of ground improvement by means of soil flushing systems. Incorporated technique with prefabricated vertical drains have been used for dewatering from fine-grained soils. The laboratory model tests were performed by using the flushing tracer solutions for silty soils and recorded the tracer concentration changes with the elapsed time and flow rates. A mathematical model for prediction of contaminant transport using the PVD technology has been developed. The clean-up times for the predictions on both soil condition indicate more of a sensitivity to the dispersivity parameter than to the extracted flow rate and vertical velocity parameters. Based on the results of the analyses, numerical analysis indicate that the most important factor to the in-situ soil remediation in prefabricated vertical drain system is the effective diameter of contaminated soil.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.259-261
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• 2008

GIS-based spatial data integration tasks have used exhaustive thematic maps generated from sparsely sampled data or satellite-based exhaustive data. Due to a simplification of reality and error in mapping procedures, such spatial data are usually imperfect and of different accuracy. The objective of this study is to carry out a sensitivity analysis in connection with input topographic data for landslide hazard mapping. Two different types of elevation estimates, elevation spot heights and a DEM from ASTER stereo images are considered. The geostatistical framework of kriging is applied for generating more reliable elevation estimates from both sparse elevation spot heights and exhaustive ASTER-based elevation values. The effects of different accuracy arising from different terrain-related maps on the prediction performance of landslide hazard are illustrated from a case study of Boeun, Korea.

• International Journal of Concrete Structures and Materials
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• 제5권1호
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• pp.29-33
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• 2011

This article employs Least Square Support Vector Machine (LSSVM) for determination of fracture parameters of concrete: critical stress intensity factor ($K_{Ic}^s$) and the critical crack tip opening displacement ($CTOD_c$). LSSVM that is firmly based on the theory of statistical learning theory uses regression technique. The results are compared with a widely used Artificial Neural Network (ANN) Models of LSSVM have been developed for prediction of $K_{Ic}^s$ and $CTOD_c$, and then a sensitivity analysis has been performed to investigate the importance of the input parameters. Equations have been also developed for determination of $K_{Ic}^s$ and $CTOD_c$. The developed LSSVM also gives error bar. The results show that the developed model of LSSVM is very predictable in order to determine fracture parameters of concrete.

• Smart Structures and Systems
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• 제16권3호
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• pp.497-519
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• 2015

This study discusses the use of Adaptive-Network-Based-Fuzzy-Inference-System (ANFIS) in predicting the shear strength of reinforced-concrete deep beams. 139 experimental data have been collected from renowned publications on simply supported high strength concrete deep beams. The results show that the ANFIS has strong potential as a feasible tool for predicting the shear strength of deep beams within the range of the considered input parameters. ANFIS's results are highly accurate, precise and therefore, more satisfactory. Based on the Sensitivity analysis, the shear span to depth ratio (a/d) and concrete cylinder strength ($f_c^{\prime}$) have major influence on the shear strength prediction of deep beams. The parametric study confirms the increase in shear strength of deep beams with an equal increase in the concrete strength and decrease in the shear span to-depth-ratio.

• 소성∙가공
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• 제12권4호
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• pp.290-295
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• 2003

High temperature deformation behavior and prediction of final microstructure after forming of Ti-6Al-4V alloy were investigated in this study. Equiaxed and Widmanstatten microstructures of Ti-6Al-4V alloys were prepared as initial microstructures and compression tests were performed to obtain the flow curves at high temperatures (700∼110$0^{\circ}C$) and various strain rates (10$^{-4}$ ∼10$^2$/s). From the results of compression test, strain rate sensitivity (m) and activation energy (Q) were calculated and used to establish constitutive equation. To predict the final microstructure after farming, finite element analysis was performed considering the microstructural parameters such as grain size and volume fraction of second phase.

• 한국화재소방학회논문지
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• 제3권2호
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• pp.11-19
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• 1989

In this study, the independent variables are the floor plan configulation. The dependent variables are the occupant's egress behavior, especially spatial movement pattern, and life - safety performance of building. Fire events were simulated on single story of office building. Simulation run for allowable secaping thime(180 seconds) arbitrarily selected, and involved 48 occupants. The major findings Pre as follows. 1) Computer simulation model suggested in this study can be used as the Preoccupancy evaluation method of the life-safety performance for architectural design based on prediction of occupants' egress behavior in the levels of validity and sensitivity, 2) Sucess or failure in occupants' escape is determined by decreasing walking speed caused by jamming at exits or over crowded corridor, and increasing route length caused by running about in confusion at each subdivision and corridor. 3) In floor plan configuration which safe areas located at the extreme ends of the corridor, cellular floor planning have to be avoided preventing jamming and running about in confusion at overcrowded corridor.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 봄 학술논문 발표대회
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• pp.33-34
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• 2020

Unit-water content is an element directly related to durability and unit-water content of concrete used at construction site has a great effect on the durability of construction structure. Many methods are being discussed for more convenient and accurate measurements of unit-water content. Therefore, an experimental study was conducted on the prediction of unit-water content using ultrasonic equipment. Depending on the amount of cement in cement paste, the speed of ultrasonic waves varies and the experiment will be carried out using the same reception sensitivity in the future.

• 대한방사선기술학회지:방사선기술과학
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• 제47권4호
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• pp.263-270
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• 2024

The objective of this study was to evaluate the accuracy and impact of leaf open time (LOT) and pitch using various machine learning models on EBT film-based delivery quality assurance (DQA) performed on 211 patients of helical tomotherapy (HT). We randomly selected passed (n=191) and failed (n=20) DQA measurements to evaluate the accuracy of the k-nearest neighbor (KNN), support vector machine (SVM), naive Bayes (NB) and logistic regression (LR) models using scale-dependent metrics such as the coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). We evaluated the performance of the four prediction models in terms of the accuracy, precision, sensitivity, and F1-score using a confusion matrix, finding the NB and LR models to achieve optimal results. The results of this study are expected to reduce the workload of medical physicists and dosimetrists by predicting DQA results according to LOT and pitch in advance.

• Structural Engineering and Mechanics
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• 제49권2호
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• pp.203-223
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• 2014

The extensive use of prestressed reinforced concrete (PSC) highway bridges in marine environment drastically increases the sensitivity to both fatigue-and corrosion-induced damage of their critical structural components during their service lives. Within this scenario, an integrated method that is capable of evaluating the fatigue reliability, identifying a condition-based maintenance, and predicting the remaining service life of its critical components is therefore needed. To accomplish this goal, a procedure for fatigue reliability prediction of PSC highway bridges is proposed in the present study. Vehicle-bridge coupling vibration analysis is performed for obtaining the equivalent moment ranges of critical section of bridges under typical fatigue truck models. Three-dimensional nonlinear mathematical models of fatigue trucks are simplified as an eleven-degree-of-freedom system. Road surface roughness is simulated as zero-mean stationary Gaussian random processes using the trigonometric series method. The time-dependent stress-concentration factors of reinforcing bars and prestressing tendons are accounted for more accurate stress ranges determination. The limit state functions are constructed according to the Miner's linear damage rule, the time-dependent S-N curves of prestressing tendons and the site-specific stress cycle prediction. The effectiveness of the methodology framework is demonstrated to a T-type simple supported multi-girder bridge for fatigue reliability evaluation.

• Computers and Concrete
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• 제12권2호
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• pp.151-167
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• 2013

Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.