• Steel and Composite Structures
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• v.48 no.2
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• pp.163-177
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• 2023

Complex and intricate preparation techniques, the imperative for utmost precision and sensitivity in instrumentation, premature sample failure, and fragile specimens collectively contribute to the arduous task of measuring the fracture toughness of concrete in the laboratory. The objective of this research is to introduce and refine an equation based on the gene expression programming (GEP) method to calculate the fracture toughness of reinforced concrete, thereby minimizing the need for costly and time-consuming laboratory experiments. To accomplish this, various types of reinforced concrete, each incorporating distinct ratios of fibers and additives, were subjected to diverse loading angles relative to the initial crack (α) in order to ascertain the effective fracture toughness (Keff) of 660 samples utilizing the central straight notched Brazilian disc (CSNBD) test. Within the datasets, six pivotal input factors influencing the Keff of concrete, namely sample type (ST), diameter (D), thickness (t), length (L), force (F), and α, were taken into account. The ST and α parameters represent crucial inputs in the model presented in this study, marking the first instance that their influence has been examined via the CSNBD test. Of the 660 datasets, 460 were utilized for training purposes, while 100 each were allotted for testing and validation of the model. The GEP model was fine-tuned based on the training datasets, and its efficacy was evaluated using the separate test and validation datasets. In subsequent stages, the GEP model was optimized, yielding the most robust models. Ultimately, an equation was derived by averaging the most exemplary models, providing a means to predict the Keff parameter. This averaged equation exhibited exceptional proficiency in predicting the Keff of concrete. The significance of this work lies in the possibility of obtaining the Keff parameter without investing copious amounts of time and resources into the CSNBD test, simply by inputting the relevant parameters into the equation derived for diverse samples of reinforced concrete subject to varied loading angles.

• The Journal of Engineering Geology
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• v.23 no.2
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• pp.137-147
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• 2013

For the effective management of groundwater resources, it is necessary to predict groundwater level fluctuations in response to rainfall events. In the present study, time series models using artificial neural networks (ANNs) and support vector machines (SVMs) have been developed and applied to groundwater level data from the Gasan, Shingwang, and Cheongseong stations of the National Groundwater Monitoring Network. We designed four types of model according to input structure and compared their performances. The results show that the rainfall input model is not effective, especially for the prediction of groundwater recession behavior; however, the rainfall-groundwater input model is effective for the entire prediction stage, yielding a high model accuracy. Recursive prediction models were also effective, yielding correlation coefficients of 0.75-0.95 with observed values. The prediction errors were highest for Shingwang station, where the cross-correlation coefficient is lowest among the stations. Overall, the model performance of SVM models was slightly higher than that of ANN models for all cases. Assessment of the model parameter uncertainty of the recursive prediction models, using the ratio of errors in the validation stage to that in the calibration stage, showed that the range of the ratio is much narrower for the SVM models than for the ANN models, which implies that the SVM models are more stable and effective for the present case studies.

• Journal of the Korean Geotechnical Society
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• v.27 no.9
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• pp.13-24
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• 2011

The renowned Terzaghi's one-dimensional consolidation theory is not applicable to quantification of time-rate settlement for highly deformable soft clays such as dredged soil deposits. To deal with this special condition, a non-linear finite strain consolidation theory should be adopted to predict the settlement of dredged soil deposits including self-weight and surcharge-induced consolidation. It is of importance to determine the zero effective stress void ratio ($e_{00}$), which is the void ratio at effective stress equal to zero, and the relationships of void ratio-effective stress and of void ratio-hydraulic conductivity for characterizing non-linear finite strain consolidation behavior for deformable dredged soil deposits. The zero effective stress void ratio means a transitional status from sedimentation to self-weight consolidation of dredged soils. In this paper, laboratory procedures and equipments are introduced to measure such key parameters in the non-linear finite strain consolidation analysis. In addition, the non-linear finite strain consolidation parameters of the Incheon clay and kaolinite are evaluated with the aid of the proposed methods in this paper, which will be used as input parameters for the non-linear finite strain consolidation analyses being performed in the companion paper.

• Journal of the Korean Geotechnical Society
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• v.29 no.1
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• pp.93-107
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• 2013

Thermal conduction of the particulate composites or granular materials can be widely used in porous materials and geotechnical engineering. And it has continued to develop "effective thermal conductivity" of medium by modeling energy relationship among particles in medium. This study focuses on the development of the effective thermal conductivity at the unsaturated conditions of soils using the modified network model approach assisted by synthetic 3D random packed systems (DEM method, Discrete Element Method) at the particle scale. To verify the network model, three kinds of glass beads and the Jumunjin sand are used to obtain experimental values at various unsaturated conditions. The PPE (Pressure Plate Extractor) test is then performed to obtain SWCC (Soil-Water Characteristic Curve) of soil samples. In the modified network model, SWCC is used to adjust the equivalent radius of thermal cylinder at contact area between particles. And cutoff range parameter to define the effective zone is also adjusted according to the SWCC at given conditions. From a series of laboratory tests and the proposed network model, the modified network model which adopts a SWCC shows a good agreement in modeling thermal conductivity of granular soils at given conditions. And an empirical correlation between the fraction of the mean radius (${\chi}$) and thermal conductivity at given saturated condition is provided, which can be used to expect thermal conductivity of the granular soils, to estimate thermal conductivity of granular soils.

• Magazine of the Korean Society of Agricultural Engineers
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• v.29 no.4
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• pp.59-72
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• 1987

Although long-term runoff analysis is important as much as flood analysis in the design of water works, the technological level of the former is relatively lower than that of the latter. In this respect, the precise estimation model for the volume of successive runoff should he developed as soon as possible. Up to now, in Korea, Gajiyama's formula has been widely used in long-term runoff analysis, which has many problems in applying in real situation. On the other hand, in flood analysis, unit hydrograph method has been exclusively used. Therefore, this study aims at trying to apply unit hydrograph method in long-term runoff analysis for the betterment of its estimation. Four test catchment areas were selected ; Maesan area in Namlum river as a representative area of Han river system, Cheongju area in Musim river as one of Geum river system, Hwasun area in Hwasun river as one of Yongsan river system, and Supyung area in Geum river as one of Nakdong river system. In the analysis of unit hydrograph, seperation of effective rainfall was carried out firstly. Considering that effective rainfall and moisture condition of catchrnent area are inside and outside of a phenomenon respectively and the latter is not considered in the analysis, Initial base flow(qb)was selected as an index of moisture condition. At the same time, basic equation(Eq.7) was established, in which qb can take a role as a parameter in relating between cumulative rainfall(P) and cumulative loss of rainfall(Ld). Based on the above equation, computer program for estimation model of qbwas seperately developed according to the range of qb, Developed model was applied to measured hydrographs and hyetographs for total 10 years in 4 test areas and effective rainfall was estimated. Estimation precision of model was checked as shown in Tab- 6 and Fig.8. In the next stage, based on the estimated effective rainfall(R) and runoff(Qd), a runoff distribution ratio was calculated for each teat area using by computerised least square method and used in making unit hydrographs in each test area. Significance of induced hydrographs was tested by checking the relative errors between estimated and measured runoff volume(Tab-9, 10). According to the results, runoff estimation error by unit hydrograph itself was merely 2 or 3 %, but other 2 or 3 % of error proved to be transferred error in the seperation of effective rainfall. In this study, special attentioning point is that, in spite of different river systems and forest conditions of test areas, standardized unit hydrographs for them have very similar curve shape, which can be explained by having similar catchinent characteristics such as stream length, catchinent area, slope, and vegetation intensity. That fact should be treated as important factor ingeneralization of unit hydrograph method.

• KSBB Journal
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• v.15 no.3
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• pp.299-306
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• 2000

Concern for the effects of toxic chemicals on the environment leads the search for better bioassay test organisms and test procedures. Photobacterium phosphoreum was used successfully as a test organism and the luminometer detection technique was an effective and simple method for determining the concentration of toxic chemicals. With EC50 a total of 14 chlorine substituted phenols benzenes and ethanes were used for the experiments. The test results showed that the toxicity to P. phosphoreum increased in the order of phenol > benzene > ethane and the toxicity also increased with the number of chlorine substitution. Quantitative structure activity relationship (QSARO) model can be used to predict EC50 to save time and endeavor. Correlation was well established with the QSAR parameters such as log P, log S and solvatochromic parameter(Vi/100 $\pi$, ${\beta}$m and am). The QSAR modeling was used with multi-regression analysis and mono-regression analysis. These analyses resulted in the following QSAR : $log EC_{50} =2.48 + 0.914 log S(n=9 R2=85.5% RE=0.378) log EC_{50}=0.35 - 4.48 Vi/100 + 2.84 \pi^* +9.46{\beta}m-4.48am (n =14 R2=98.2% RE=0.012) log EC_{50} =2.64 -1.66 log P(n=5, R2=98.8% RE=0.16) log EC_{50}=3.44 -1.09 log P(n=9 R2= 80.8% Re=0.207)$

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.107-114
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• 2001

This paper describes Connected Digit Recognition with Considering Acoustic Feature in Korea. The recognition rate of connected digit is usually lower than word recognition. Therefore, speech feature parameter and acoustic feature are employed to make robust model for digit, and we could confirm the effect of Considering. Acoustic Feature throughout the experience of recognition. We used KLE 4 connected digit as database and 19 continuous distributed HMM as PLUs(Phoneme Like Units) using phonetical rules. For recognition experience, we have tested two cases. The first case, we used usual method like using Mel-Cepstrum and Regressive Coefficient for constructing phoneme model. The second case, we used expanded feature parameter and acoustic feature for constructing phoneme model. In both case, we employed OPDP(One Pass Dynamic Programming) and FSA(Finite State Automata) for recognition tests. When appling FSN for recognition, we applied various acoustic features. As the result, we could get 55.4% recognition rate for Mel-Cepstrum, and 67.4% for Mel-Cepstrum and Regressive Coefficient. Also, we could get 74.3% recognition rate for expanded feature parameter, and 75.4% for applying acoustic feature. Since, the case of applying acoustic feature got better result than former method, we could make certain that suggested method is effective for connected digit recognition in korean.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.77-90
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• 2018

Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.

• The Journal of Internal Korean Medicine
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• v.25 no.2
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• pp.195-201
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• 2004

Background and Purpose : Hypertension is one of the modifiable risk factors for stroke. Lowering blood pressure is a primary or secondary preventative measure for stroke. This study aims to assess the efficacy of Chunghyul-dan(Qingxue-dan) in stage 1 hypertensive patients who have suffered a stroke by 24 hour ambulatory blood pressure monitoring(24ABPM). Subjects& Methods : We enrolled 40 hospitalized stroke patients with stage 1 hypertension and divided them into 2 groups by stratified randomization; group A took 1200mg of Chunghyul-dan(Qingxue-dan) at 8:00 a.m. for two weeks without changing herbal medicine, and group B was the control group. 28 patients were included in the final analysis(15 in group A. 13 in group B). Blood pressure is monitored from 8:00 am to 7:30 am every 30 minutes for 24 hours. Blood pressure was monitored two times at baseline and again two weeks later. We used 3 parameters for evaluating the efficacy of Chunghyul-dan(Qingxue-dan); The first parameter is change from baseline to two weeks later in blood pressure and pulse rate. The second parameter is the trough/peak ratio(TPR) and smoothness index(SI). The third parameter is antihypertensive rate by antihypertensive efficacy guideline. Results : There is no significant difference in the baseline assessment hetween the two groups. Systolic blood pressure $(141.37{\pm}8.96\;mmHg\;vs\;132.28{\pm}9.46\;mmHg)$ decreased after two weeks of 1200mg(P=0.03) intake of Chunghyul-dan(Qingxue-dan). Systolic TPR and SI was 0.87 and 1.04 in group A. Antihypertensive rate was higher in group A. Conclusion: These results suggest that 1200mg doses of Clunghyul-dan(Qingxue-dan) is an effective antihypertensive agent on stage 1 hypertension patients who have suffered a stroke.