• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.45-53
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• 2019

As the urbanization progresses, the demand for parking in large cities surges compared with that for parking lots. However, the space for securing a parking lot in a large city is physically limited and a budget problem also arises. Therefore, a shared parking system that can utilize existing parking lots is becoming important. This study was carried out to analyze the parking area efficiently for the residents parking area. As a result of the questionnaire survey, it was the most frequent passage to work / commuting and business purposes. The most important factors in parking were 'Parking charge', 'Walking distance to the destination after parking', 'Parking lot searching time' Approximately 46 % of the respondents were female. As a result of the quantitative analysis of the factors influencing the potential use of the intention to use as a dependent variable, it was analyzed that the policy was effective to reduce the parking fee to less than 500 won per 10 minutes and take about 3-6 minutes to search.

• Journal of Forest and Environmental Science
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• v.35 no.3
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• pp.205-211
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• 2019

The objective of this research was to assess the amount of carbon stock of coarse woody debris (CWD) in Bombo-Lumene Reserve. Data on lying CWD was collected on 35 circular sampling plots using Line Intersect Sampling (LIS) method. A total of 230 samples CWD (${\geq}10cm$ diameter) were inventoried. The mean carbon stocks of CWD was $29.48Mg\;C\;ha^{-1}$, ranging from 4.32 to $73.54Mg\;C\;ha^{-1}$. The CWD carbon stocks displayed a wide range of variation in decay states. The allocation of CWD among the decay class of all the CWD samples reveals that the most important classes were class 1 and class 3 with 323.66 and $321.96Mg\;C\;ha^{-1}$, followed by class 4 with 264.56 and the last one was class 2 with $121.72Mg\;C\;ha^{-1}$. The results suggested that the dead wood component is important in carbon sequestration and should be taken into consideration for quantification of carbon stocks not only in Bombo-Lumene Reserve, but in all forest ecosystems in the Democratic Republic of Congo.

• Journal of Forest and Environmental Science
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• v.37 no.2
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• pp.116-127
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• 2021

Predicting carbon distribution of soil aggregates is difficult due to complexity in organo-mineral formation. This limits global warming mitigation through soil carbon sequestration. Therefore, knowledge of land use effect on carbon stabilization requires quantification of soil mineral cations. The study was conducted to quantify carbon and base cations on soil mineral fractions in Natural Forest, Plantation Forest and Farm Land. Five 0.09 ha were demarcated alternately along 500 m long transect with an interval of 50 m in Natural Forest (NF), Plantation Forest (PF) and Farm Land (FL). Soil samples were collected with soil cores at 0-15, 15-30 and 30-45 cm depths in each plot. Soil core samples were oven-dried at 105℃ and soil bulk densities were computed. Sample (100 g) of each soil core was separated into >2.0, 2.0-1.0, 1.0-0.5, 0.5-0.05 and <0.05 mm aggregates using dry sieve procedure and proportion determined. Carbon concentration of soil aggregates was determined using Loss-on-ignition method. Mineral fractions of soil depths were obtained using dispersion, sequential extraction and sedimentation methods of composite soil samples and sieved into <0.05 and >0.05 mm fractions. Cation exchange capacity of two mineral fractions was measured using spectrophotometry method. Data collected were analysed using descriptive and ANOVA at α_0.05. Silt and sand particle size decreased while clay increased with increase in soil depth in NF and PF. Subsoil depth contained highest carbon stock in the PF. Carbon concentration increased with decrease in aggregate size in soil depths of NF and FL. Micro- (1-0.5, 0.5-0.05 and <0.05 mm) and macro-aggregates (>2.0 and 2-1.0 mm) were saturated with soil carbon in NF and FL, respectively. Cation exchange capacity of <0.05 mm was higher than >0.05 mm in soil depths of PF and FL. Fine silt (<0.05 mm) determine the cation exchange capacity in soil depths. Land use and mineral size influence the carbon and cation exchange capacity of Gambari Forest Reserve.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.508-520
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• 2020

Dam reservoirs have been reported to contribute significantly to global carbon emissions, but unlike natural lakes, there is considerable uncertainty in calculating carbon emissions due to the complex of emission pathways. In particular, the method of calculating carbon dioxide (CO2) net atmospheric flux (NAF) based on a simple gas exchange theory from sporadic data has limitations in explaining the spatiotemporal variations in the CO2 flux in stratified reservoirs. This study was aimed to analyze the spatial and temporal CO2 distribution and mass balance in Daecheong Reservoir, located in the mid-latitude monsoon climate zone, by applying a two-dimensional hydrodynamic and water quality model (CE-QUAL-W2). Simulation results showed that the Daecheong Reservoir is a heterotrophic system in which CO2 is supersaturated as a whole and releases CO2 to the atmosphere. Spatially, CO2 emissions were greater in the lacustrine zone than in the riverine and transition zones. In terms of time, CO2 emissions changed dynamically according to the temporal stratification structure of the reservoir and temporal variations of algae biomass. CO2 emissions were greater at night than during the day and were seasonally greatest in winter. The CO2 NAF calculated by the CE-QUAL-W2 model and the gas exchange theory showed a similar range, but there was a difference in the point of occurrence of the peak value. The findings provide useful information to improve the quantification of CO2 emissions from reservoirs. In order to reduce the uncertainty in the estimation of reservoir carbon emissions, more precise monitoring in time and space is required.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.107-116
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• 2022

Recently, research has been actively conducted on the technology of inspection facilities through image-based analysis assessment of human-inaccessible facilities. This research was conducted to study the conditions of deep learning-based imaging data on bridges and to develop an evaluation prototype program for bridges. To develop a deep learning-based bridge damage detection prototype, the Semantic Segmentation model, which enables damage detection and quantification among deep learning models, applied Mask-RCNN and constructed learning data 5,140 (including open-data) and labeling suitable for damage types. As a result of performance modeling verification, precision and reproduction rate analysis of concrete cracks, stripping/slapping, rebar exposure and paint stripping showed that the precision was 95.2 %, and the recall was 93.8 %. A 2^nd performance verification was performed on onsite data of crack concrete using damage rate of bridge members.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1223-1234
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• 2022

Efforts for reducing greenhouse gas emission coping with climate change have also been performed in the field of water and wastewater works. In particular, the technical development for reducing energy has been applied in operating water distribution system. The reduction of energy in water distribution system can be achieved by reducing structural loss induced by topographic variation and operational loss induced by leakage and friction. However, both analytical and numerical approaches for analyzing energy budget of water distribution system has been challengeable because energy components are affected by the complex interaction of affecting factors. This research drew mathematical equations for 5 types of state (hypothetical, ideal, leak-included ideal, leak-excluded real, and real), which depend on the assumptions of topographic variation, leakage, and friction. Furthermore, the derived equations are schematically illustrated and applied into simple water network. The suggested method makes water utilities quantify, classify, and evaluate the energy of water distribution system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.573-580
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• 2006

The Saturation headway is a major parameter in estimating the intersection capacity and setting the signal timing. But Existing algorithms are still far from being robust in dealing with factors related to the variation of saturation headways at signalized intersections. So this study apply the fuzzy inference system using ANFIS. The ANFIS provides a method for the fuzzy modeling procedure to learn information about a data set, in order to compute the membership function parameters that best allow the associated fuzzy inference system to track the given input/output data. The climate conditions and the degree of brightness were chosen as the input variables when the rate of heavy vehicles is 10-25 %. These factors have the uncertain nature in quantification, which is the reason why these are chosen as the fuzzy variables. A neuro-fuzzy inference model to estimate saturation headways at signalized intersections was constructed in this study. Evaluating the model using the statistics of $R^2$, MAE and MSE, it was shown that the explainability of the model was very high, the values of the statistics being 0.993, 0.0289, 0.0173 respectively.

• Journal of Korean Society on Water Environment
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• v.40 no.1
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• pp.36-46
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• 2024

Microplastics in water resources have been recognized as a serious problem. The discharge of microplastics from wastewater treatment plants is considered a major contributor to environmental pollution in water resources. However, a reliable analytical method for quantifying microplastics in wastewater treatment plants has not yet been established. This study proposes a reliable, quick, and easy analytical method for quantifying microplastics. For the removal of organic particles, preprocessing steps were applied including oxidation, sonication, washing, and sieving. Nile Red staining was used to visualize microplastics, and quantitative analysis was conducted using fluorescent imaging. The stained microplastics were ultimately quantified through image analysis software. Among the preprocessing steps, sonication and washing stages were particularly effective in efficiently removing interfering substances from wastewater, enhancing the accuracy of the microplastic analysis. Additionally, various solvents (methanol, acetone, and N-hexane) for the Nile Red staining solution were tested. When N-hexane was applied as the solvent, the quantity of stained microplastics was lower compared to methanol and acetone. This suggests that N-hexane has a greater potential of reducing false staining and counting of non-plastic particles. In summary, this research demonstrates a robust method for quantifying microplastics in wastewater treatment plants by employing effective preprocessing steps and optimizing the staining process with Nile Red and N-hexane.

• 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.

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.1-8
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• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.