• Journal of Soil and Groundwater Environment
• /
• v.12 no.1
• /
• pp.53-63
• /
• 2007

Soil risk assessment models were used to determine the goals of soil remediation and to establish the soil quality standards in developed countries. Recently, Korean Ministry of Environment prepared the guideline for soil risk assessment. Soil risk assessment model applicable to Korean situation will be needed in the near future. In this study, three models for soil risk assessment were extensively compared to suggest the fundamental components that required for the soil risk assessment in Korea. The models considered in this study were CalTOX in the United States, CLEA (Contaminated Land Exposure Assessment) in the United Kingdom, and CSOIL in the Netherlands. The major exposure routes and the intake estimation equations suitable for Korean situation were suggested. The exposure routes suggested were intake of the crops, underground water, indoor outdoor soil ingestion, dust inhalation and a volatile matter inhalation. The equations for intake estimation used in CalTOX and CSOIL seem to be applicable for the calculation of the human intake in Korea.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.39 no.6
• /
• pp.110-117
• /
• 2011

In order to study the growth models between the growth of Sedum takevimense and growth rate in soil with three types of mix ratios, this experiment was carried out on April 3rd, 2011. A nonlinearity regression analysis was performed using the Logistic and Gompertz models by SPSS. According to the study of growth models of Sedum takevimense, the process of growth and management methods after over-wintering were explicitly determined. According to the measured values, the growth in the soil of $P_1P_2V_1$ and $P_2P_1V_1$ was better than that of $P_1$. Particularly, the average length of Sedum takevimense in the soil of $P_1P_2V_1$ was about twice as great as that in the $P_1$. The fitness test of the two growth models was: The predicted value and measured value were separately compared and analysed, the average fitting precision $R^2$ of the Logistic models was 0.995, but the average $R^2$ of the Gompertz models was below 0.978, which showed that the Logistic models were better than the Gompertz models. The growth models also showed that the growth time of Sedum takevimense was divided into three: rapid, most rapid and slow. When managed in the rapid and the most rapid time, it will grow better.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.53 no.1
• /
• pp.71-81
• /
• 2011

Due to increased human activities and intensive rainfall events in a watershed, soil erosion and sediment transport have been hot issues in many areas of the world. To evaluate soil erosion problems spatially and temporarily, many computer models have been developed and evaluated over the years. However, it would not be reasonable to apply the model to a watershed if topography and environment are different to some degrees. Also, source codes of these models are not always public for modification. The ArcGIS model builder provides ease-of-use interface to develop model by linking several processes and input/output data together. In addition, it would be much easier to modify/enhance the model developed by others. Thus, simple model was developed to decide soil erosion hot spot areas using ArcGIS model builder tool in this study. This tool was applied to a watershed to evaluate model performance. It was found that sediment yield was estimated to be 13.7 ton/ha/yr at the most severe soil erosion hot spot area in the study watershed. As shown in this study, the ArcGIS model builder is an efficient tool to develop simple models without professional programming abilities. The model, developed in this study, is available at http://www.EnvSys.co.kr/~sateec/toolbox for free download. This tool can be easily modified for further enhancement with simple operations within ArcGIS model builder interface. Although very simple soil erosion and sediment yield were developed using model builder and applied to study watershed for soil erosion hot spot area in this study. The approaches shown in this study provides insights for model development and code sharing for the researchers in the related areas.

• Earthquakes and Structures
• /
• v.20 no.1
• /
• pp.55-70
• /
• 2021

In urban cities, buildings were built in the neighborhood, these buildings influence each other through structure-soilstructure interaction (SSSI) and seismic pounding due to limited separation distance in-between. Generally, the effects of the interaction between soil and structure are disregarded during seismic design and analysis of superstructure. However, the system of soil-base adversely changes structural behavior and response demands. Thus, the vibration characteristics plus the seismic response of a building are not able to be independent of those in adjacent buildings. The interaction between structure, soil, and structure investigates the action of the attendance of adjacent buildings to the others by the interaction effect of the sub-soil under dynamic disturbances. The main purpose of this research is to analyze the effects of SSSI and seismic pounding on the behavior of adjacent buildings. The response of a single structure or two adjacent structures with shallow raft base lying on soft soil are studied. Three dimensions finite element models are developed to investigate the effects of pounding; gap distance; conditions of soil; stories number; a mass of adjacent building and ground excitation frequency on the seismic responses and vibration characteristics of the structures. The variation in the story displacement, story shear, and story moment responses demands are studied to evaluate the presence effect of the adjacent buildings. Numerical results acquired using conditions of soil models are compared with the condition of fixed support and adjacent building models to a single building model. The peak responses of story displacement, story moment, and story shear are studied.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.1
• /
• pp.79-89
• /
• 2022

Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.

• Geomechanics and Engineering
• /
• v.33 no.5
• /
• pp.487-506
• /
• 2023

Looking from the past to the present, the earthquakes can be said to be type of disaster with most casualties among natural disasters. Soil liquefaction, which occurs under repeated loads such as earthquakes, plays a major role in these casualties. In this study, analytical equation models were developed to predict the probability of occurrence of soil liquefaction. In this context, the parameters effective in liquefaction were determined out of 170 data sets taken from the real field conditions of past earthquakes, using WEKA decision tree. Linear, Exponential, Power and Quadratic models have been developed based on the identified earthquake and ground parameters using Krill Herd algorithm. The Exponential model, among the models including the magnitude of the earthquake, fine grain ratio, effective stress, standard penetration test impact number and maximum ground acceleration parameters, gave the most successful results in predicting the fields with and without the occurrence of liquefaction. This proposed model enables the researchers to predict the liquefaction potential of the soil in advance according to different earthquake scenarios. In this context, measures can be realized in regions with the high potential of liquefaction and these measures can significantly reduce the casualties in the event of a new earthquake.

• Steel and Composite Structures
• /
• v.49 no.2
• /
• pp.231-244
• /
• 2023

Many of the recent investigations in the field of geotechnical engineering focused on the bearing capacity theories of multilayered soil. A number of factors affect the bearing capacity of the soil, such as soil properties, applied overburden stress, soil layer thickness beneath the footing, and type of design analysis. An extensive number of finite element model (FEM) simulation was performed on a prototype slope with various abovementioned terms. Furthermore, several non-linear artificial intelligence (AI) models are developed, and the best possible neural network system is presented. The data set is from 3443 measured full-scale finite element modeling (FEM) results of a circular shallow footing analysis placed on layered cohesionless soil. The result is used for both training (75% selected randomly) and testing (25% selected randomly) the models. The results from the predicted models are evaluated and compared using different statistical indices (R² and RMSE) and the most accurate model BBO (R²=0.9481, RMSE=4.71878 for training and R²=0.94355, RMSE=5.1338 for testing) and TLBO (R²=0.948, RMSE=4.70822 for training and R²=0.94341, RMSE=5.13991 for testing) are presented as a simple, applicable formula.

• Geomechanics and Engineering
• /
• v.22 no.5
• /
• pp.397-414
• /
• 2020

In this paper, practical predictive models for soil shear strength parameters are proposed. As cohesion and internal friction angle are of essential shear strength parameters in any geotechnical studies, we try to predict them via artificial neural network (ANN) and neuro-imperialism approaches. The proposed models was based on the result of a series of consolidated undrained triaxial tests were conducted on reinforced sandy soil. The experimental program surveys the increase in internal friction angle of sandy soil due to addition of polypropylene fibers with different lengths and percentages. According to the result of the experimental study, the most important parameters impact on internal friction angle i.e., fiber percentage, fiber length, deviator stress, and pore water pressure were selected as predictive model inputs. The inputs were used to construct several ANN and neuro-imperialism models and a series of statistical indices were calculated to evaluate the prediction accuracy of the developed models. Both simulation results and the values of computed indices confirm that the newly-proposed neuro-imperialism model performs noticeably better comparing to the proposed ANN model. While neuro-imperialism model has training and test error values of 0.068 and 0.094, respectively, ANN model give error values of 0.083 for training sets and 0.26 for testing sets. Therefore, the neuro-imperialism can provide a new applicable model to effectively predict the internal friction angle of fiber-reinforced sandy soil.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
• /
• 2003.04a
• /
• pp.25-39
• /
• 2003

Risk and exposure assessment for subsurface environment is very important for both aspects of health and environmental protection as well as making decision of remedial goal for engineering activities. Exposure due to hazardous chemicals in the subsurface environment is essential to assess risk lev121 to individual person, especially from soil and groundwater environmental media. In this paper, the status of soil and groundwater contamination is presented to discuss on the problem for environmental risk assessment. The methodologies of fate and exposure models are also discussed by conducting the case studies of exposure assessment for heavy metals, organic compounds, and dioxin compounds. In addition, the structure of exposure models and available data for model calculation are examined to make clear more realistic exposure scenarios and the application to the practical environmental issues. Three kinds of advanced remediation techniques for soil and groundwater contamination are described in this paper, The most practical method for VOCs is the bio-remediation technique in which biological process due to consortium of microorganisms can be applied. For more effective remediation of soil contaminated by heavy metals we have adopted the soil flushing technique and clean-up system using electro-kinetic method. We have also developed the advanced techniques of geo-melting method for soil contaminated by DXNs and PCB compounds. These techniques are planed to introduce and to apply for a lot of contaminated sites in Japan.

• Water Engineering Research
• /
• v.1 no.3
• /
• pp.209-222
• /
• 2000

A spectral formalism was applied to quantify the sampling errors due to spatial and/or temporal gaps in soil moisture measurements. The lack of temporal measurements of the two-dimensional soil moisture field makes it difficult to compute the spectra directly from observed records. Therefore, the space-time soil moisture spectra derived by stochastic models of rainfall and soil moisture was used in their record. Parameters for both models were tuned with Southern Great Plains Hydrology Experiment(SGP'97) data and the Oklahoma Mesonet data. The structure of soil moisture data is discrete in space and time. A design filter was developed to compute the sampling errors for discrete measurements in space and time. This filter has the advantage in its general form applicable for all kinds of sampling designs. Sampling errors of the soil moisture estimation during the SGP'97 Hydrology Experiment period were estimated. The sampling errors for various sampling designs such as satedlite over pass and point measurement ground probe were estimated under the climate condition between June and August 1997 and soil properties of the SGP'97 experimental area. The ground truth design was evaluated to 25km and 50km spatial gap and the temporal gap from zero to 5 days.