• Korean Journal of Construction Engineering and Management
• /
• v.25 no.4
• /
• pp.24-33
• /
• 2024

Recently, as smart construction has become more active, construction companys are evaluating their smart construction capabilities in order to transform into smart construction companies. However, the revitalization of smart construction doesn't only apply to construction companies, the level of utilization of all participants, including owners, designers, construction project managers, and construction company, must be improved. Therefore, this study aims to present a model that evaluate the building information modeling (BIM) utilization level for measuring the BIM utilization level of construction management companies in executing construction project management. In this study, an AHP questionnaire survey targeting BIM practitioners to calculate the weight of each BIM utilization item and score it to construct evaluation model and evaluate it by applying it to construction management companies are conducted. As a result of the evaluation using model, there were differences between companies in the number of BIM users, and in the qualitative evaluation, it is mainly used for interference review, constructability review, and design change management. Therefore, in order to revitalize BIM, it is believed that it is necessary to strengthen BIM utilization ability through separate training for construction manager (CMr) and to present clear utilization standards and scope of work for BIM utilization in performing construction management tasks. Consequently, evaluating more construction management companies using the model presented in this study will result in the transition of CM companies to smart construction and revitalization of BIM adoption.

• Journal of the Korea Organic Resources Recycling Association
• /
• v.17 no.4
• /
• pp.91-102
• /
• 2009

In this study, to estimate the transforming (runoff and leachate) rate of the organic fertilizer made of livestock resources to farm land, laboratory scale test was conducted and the results were obtained as follows: The runoff volume from farm land showed the tendency of increase according to the increase of rainfall intensity. The most rainfall leachated into the underground at the rainfall intensity of 20mm/hr, and rainfall of 5L or less leachated at the rainfall intensity of > 32.4 mm/hr. This shows that surface runoff largely depends on the rainfall intensity when soil characteristic and hardness are similar in each site. When liquid compost was fertilized, the surface runoff was similar with the results from the reactor fertilized by compost, and leachate flow was found to be lower than compost. The runoff ratio of contaminant parameters from farm land were BOD 0.00003,, $COD_{cr}$ 0.00006, TN 0.00056, TP 0.00011, TOC 0.00005, Especially, the runoff ratio of TN showed 10 folds higher than other parameters. On the other hand, the runoff ratio of SS showed higher value of 0.001, and colloid particles of soil caused this result rather than the leachate from compost fertilizer. At all ranges of rainfall intensity, fertilizer removal ratio by farm land was found to be 94.9~98.4% for compost and 85.8~98.1% for liquid compost in terms of BOD. For TN, it resulted in 96.6~98.4% for compost and 97.2~98.5% for liquid compost, and thus the most fertilizer from livestock resources were shown to be reduced through farm land application.

• Journal of Korea Water Resources Association
• /
• v.57 no.9
• /
• pp.615-626
• /
• 2024

To accurately and efficiently monitor soil moisture (SM) across South Korea, this study developed a SM estimation model that integrates the cloud computing platform Google Earth Engine (GEE) and Automated Machine Learning (AutoML). Various spatial information was utilized based on Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and the global precipitation observation satellite GPM (Global Precipitation Measurement) to test optimal input data combinations. The results indicated that GPM-based accumulated dry-days, 5-day antecedent average precipitation, NDVI (Normalized Difference Vegetation Index), the sum of LST (Land Surface Temperature) acquired during nighttime and daytime, soil properties (sand and clay content, bulk density), terrain data (elevation and slope), and seasonal classification had high feature importance. After setting the objective function (Determination of coefficient, R² ; Root Mean Square Error, RMSE; Mean Absolute Percent Error, MAPE) using AutoML for the combination of the aforementioned data, a comparative evaluation of machine learning techniques was conducted. The results revealed that tree-based models exhibited high performance, with Random Forest demonstrating the best performance (R² : 0.72, RMSE: 2.70 vol%, MAPE: 0.14).

• Journal of the Society of Disaster Information
• /
• v.20 no.3
• /
• pp.584-592
• /
• 2024

Purpose: The biggest concern in cold wave situations is that the fire extinguishing water initially supplied through dry pipes with empty pipes consumes enthalpy and freezes as it rapidly approaches the surface temperature of steel pipes that have been exposed to sub-zero outdoor air for a long time. It has no choice but to be. Method: Therefore, the study found that ice crystals were generated during transport, making it difficult to transport fire extinguishing water, and as a result of the review, when the heat load passed through the piping material, the heat loss per unit length from the piping to the surroundings was 0.946. Results: When calculating the volume of the main pipe, it was calculated that the fire extinguishing water supplied at a temperature of 15 degrees from the underground pipe would have a volume of 3.33m³ to reach the first branch point. If we calculate the heat required until this volume reaches below zero, we get 316.350 kcal. When the results were reviewed using the related formula, the time required for the fire extinguishing water to completely freeze up to the first branch of the steel pipe was found to be 3,412 seconds. Conclusion: Fire-fighting water, which must reach from the main pipe to the branch pipe and nozzle in good condition, must minimize heat loss through the pipe surface along the transfer path. To achieve this, it is necessary to supplement insulation of the main pipe and branch pipes. In this study, the use of inorganic perlite material or flame-retardant rubber foam insulation was proposed through analysis of insulation properties.

• Journal of the Korean Geotechnical Society
• /
• v.40 no.4
• /
• pp.91-103
• /
• 2024

In this study, we propose an outlier detection method that considers the spatial distribution of intelligent compaction measurement values (ICMVs) to address the high variability of ICMVs measured continuously across an entire construction area. The proposed method initially identified cases where the CMV at a specific location decreased despite an increase in the number of compaction passes. Among these, values that significantly differed from those measured within a 1.5-m radius were classified as outliers. Applying this method to CMV data obtained from field tests, we found that it effectively excluded the influence of changes in roller operating conditions unrelated to compaction quality while considering the inherent heterogeneity of the soil. However, after removing the outliers, the coefficient of variation of CMV (21.4%-26.3%) remained higher than the 20% suggested by relevant standards. Further field tests are needed to modify the proposed outlier detection method and to establish reasonable criteria for the variability of ICMV.

• Journal of Soil and Groundwater Environment
• /
• v.29 no.5
• /
• pp.27-36
• /
• 2024

This study, estimates the predicted no effect concentration (PNEC) for the protection of organisms in aquatic and soil environments, considering the mode of action of Perfluorooctanesulfonic acid (PFOS). PNECs were derived using the species sensitivity distribution (SSD) approach to estimate the hazardous concentration for 5% of species (HC5), with applying assessment factors. Chronic toxicity data on PFOS were collected through the USEPA's ECOTOX database and literature reviews, and classified by toxicity endpoints. PNECs were then derived for each of seven toxicity endpoints that met the criteria for SSD fitting. For aquatic organisms, the PNEC for PFOS, based on all available chronic toxicity data, was determined to be 0.53 ㎍/L. The PNECs for development, genetics, enzymes, growth, reproduction, population, and biochemical biomarkers were 0.28, 0.43, 0.83, 0.90, 2.17, 111.17, and 3.53 ㎍/L, respectively. The lowest PNEC was observed when the toxic endpoint was set as development, which is considered to be due to the mode of action of PFOS, known to cause developmental toxicity by disrupting the endocrine system of organisms. For soil organisms, toxicity data were insufficient to estimate PNECs for individual endpoints, so all available data were used to estimate a PNEC of 0.75 mg/kg. Estimating PNECs that consider the mode of action of contaminants is expected to reduce the likelihood of underestimating protection levels for environmental contaminants. Additionally, this study highlights the need for ecotoxicological assessments for individual toxicity endpoints of emerging contaminants, including Per- and polyfluoroalkyl substances, in soil environments.

• Ecology and Resilient Infrastructure
• /
• v.11 no.3
• /
• pp.100-109
• /
• 2024

There are various methods in surface image velocimetry that can measure the flow velocity of a river surface using footage of a river surface. Among them, spatio-temporal image velocimetry (STIV) is widely used. This paper analyzed advantages and disadvantages of two STIV methods developed by the author, correlation-based STIV (C-STIV) and FFT-based STIV (F-STIV). This study also proposed a new method, hybrid STIV (H-STIV), that could supplement the advantages and disadvantages of the two existing methods by combining them. For the analyses, 20 cases of artificial spatio-temporal images, having image displacement ranging from 0.1 px/fr to 19.0 px/fr, were prepared. As a result, F-STIV was accurate with footage containing small image displacements and C-STIV was more accurate with footage containing large image displacements. For images with medium displacement, the two methods showed similar accuracies. Based on this result, H-STIV was proposed to adopt the result of F-STIV in displacements smaller than 2.0 px/fr (image strip slope 63.4°) and the result of C-STIV in larger displacements. As a result of applying the proposed method to argumentation, it was confirmed that H-STIV could calculate the flow velocity efficiently according to the situation.

• Journal of the Korean Geosynthetics Society
• /
• v.23 no.3
• /
• pp.15-22
• /
• 2024

Predicting ground settlement during the improvement of soft ground and the construction of a structure is an crucial factor. Numerous studies have been conducted, and many prediction equations have been proposed to estimate settlement. Settlement can be calculated using the compression index of clay. In this study, data on water content, void ratio, liquid limit, plastic limit, and compression index from the Busan New Port area were collected to construct a dataset. Correlation analysis was conducted among the collected data. Machine learning algorithms, including Random Forest, Neural Network, Linear Regression, Ada Boost, and Gradient Boosting, were applied using the Orange mining program to propose compression index prediction models. The models' results were evaluated by comparing RMSE and MAPE values, which indicate error rates, and R² values, which signify the models' significance. As a result, water content showed the highest correlation, while the plastic limit showed a somewhat lower correlation than other characteristics. Among the compared models, the AdaBoost model demonstrated the best performance. As a result of comparing each model, the AdaBoost model had the lowest error rate and a large coefficient of determination.

• Journal of the Korea Organic Resources Recycling Association
• /
• v.32 no.3
• /
• pp.19-29
• /
• 2024

Total organic carbon (TOC) represents the total amount of organic carbon contained in water and is a key water quality parameter used, along with biochemical oxygen demand (BOD) and chemical oxygen demand (COD), to quantify the amount of organic matter in water. In this study, a model to predict TOC was developed using XGBoost (XGB), a representative ensemble machine learning algorithm. Independent variables for model construction included water temperature, pH, electrical conductivity, dissolved oxygen concentration, BOD, COD, suspended solids, total nitrogen, total phosphorus, and discharge. To quantitatively analyze the impact of various water quality parameters used in model construction, the feature importance of input variables was calculated. Based on the results of feature importance analysis, items with low importance were sequentially excluded to observe changes in model performance. When built by sequentially excluding items with low importance, the performance of the model showed a root mean squared error-observation standard deviation ratio (RSR) range of 0.53 to 0.55. The model that applied all input variables showed the best performance with an RSR value of 0.53. To enhance the model's field applicability, models using relatively easily measurable parameters were also built, and the performance changes were analyzed. The results showed that a model constructed using only the relatively easily measurable parameters of water temperature, electrical conductivity, pH, dissolved oxygen concentration, and suspended solids had an RSR of 0.72. This indicates that stable performance can be achieved using relatively easily measurable field water quality parameters.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.44 no.5
• /
• pp.695-705
• /
• 2024

As a result of Typhoon Hinnamnoh, several slope failures occurred in the Pohang region, it is necessary to perform infiltration and slope stability analyses due to the actual rainfall. In the failed sites, samples were collected, and the hydro-mechanical properties of unsaturated soil were examined. Modeling the actual behavior using a single-mode function characteristic curve was found to be inadequate, leading to the adoption of a dual-mode function characteristic curve. The dual-mode function showed better agreement with the water retention test data. We calculated the unsaturated hydraulic conductivity for single and dual modes and performed rainfall-induced infiltration analysis. The variations in saturation and pore water pressure were calculated due to rainfall for three landslide-prone areas, Stability analysis based on effective stress of unsaturated soil was conducted, and safety factors were computed over time steps. The dual-mode model successfully reproduced landslides triggered by Typhoon Hinnamnoh, while the single-mode model exhibited a minimum safety factor of 1.2-1.3, making slope failure unpredictable. The dual-mode model accurately predicted instability in the slope by appropriately accounting for pore water pressure variations during Typhoon.