• Computers and Concrete
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• v.15 no.2
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• pp.231-257
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• 2015

This paper discusses the spatial variability of the carbonation depth caused by the mesoscopic structure of the concrete and the influence of the spatial variability on the thickness of the concrete cover. To conduct the research, a method to generate the random aggregate structure (RAS) based on polygonal particles and a simplified numerical model of the concrete carbonation at meso-scale are firstly developed. Based on the method and model, the effect of the aggregate properties including shape, content and gradation on the spatial variability of the carbonation depth is comprehensively studied. The results show that a larger degree of the spatial variability will be obtained by using (1) the aggregates with a larger aspect ratio; (2) a larger aggregate content; (3) the gradation which has more large particles. The proper sample size and model size used in the analysis are also studied. Finally, a case study is conducted to demonstrate the influence of the spatial variability of the carbonation depth on the proper thickness of the concrete cover. The research in this paper not only provides suggestions on how to decrease the spatial variability, but also proposes the method to consider the effect of the spatial variability in designing the thickness of the concrete cover.

• Journal of Biosystems Engineering
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• v.29 no.2
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• pp.109-120
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• 2004

To obtain basic information for precision agriculture, spatial variability of rice growth condition was evaluated in 100m ${\times}$100m paddy field. The rice growth condition of four hundred locations in the field were investigated to analyze the spatial variability of their properties ; SPAD, plant length and tiller number. Geostatistical analysis was carried out to examine within-field spatial variability using semivariograms and kriged maps as well as descriptive statistics. Descriptive statistics showed that the coefficient of variation for SPAD, plant length, and tiller number exceeded 5.70 %, suggesting a relatively high variability. Geostatistical analysis indicated a high spatial dependence for all the properties except for the second tiller number. The range of spatial dependence was about 20 m for SPAD, plant length, and tiller number. Based on the results of spatial dependence, kriged maps were prepared for the properties to analyse their spatial distribution in the field. The results reflected the history of field management. In conclusion, the need for site-specific field management and possibility of precision agriculture were demonstrated even in an almost flat paddy field.

• Structural Engineering and Mechanics
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• v.23 no.3
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• pp.233-247
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• 2006

Most codes of practice state that for large in-plane structures it is necessary to account for the spatial variability of earthquake ground motion. There are essentially three effects that contribute for this variation: (i) wave passage effect, due to finite propagation velocity; (ii) incoherence effect, due to differences in superposition of waves; and (iii) the local site amplification due to spatial variation in geological conditions. This paper discusses the procedures to be undertaken in the time domain analysis of a cable-stayed bridge under spatial variability of earthquake ground motion. The artificial synthesis of correlated displacements series that simulate the earthquake load is discussed first. Next, it is described the 3D model of the International Guadiana Bridge used for running tests with seismic analysis. A comparison of the effects produced by seismic waves with different apparent propagation velocities and different geological conditions is undertaken. The results in this study show that the differences between the analysis with and without spatial variability of earthquake ground motion can be important for some displacements and internal forces, especially those influenced by symmetric modes.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.3
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• pp.55-63
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• 2012

Soil properties are not random values which is represented by mean and standard deviation but show spatial correlation. Especially, soils are highly variable in their properties and rarely homogeneous. Thus, the accuracy and reliability of probabilistic analysis results is decreased when using only one random variable as design parameter. In this paper, to consider spatial variability of soil property, one-dimensional random fields of coefficient of consolidation ($C_v$) were generated based on a Karhunen-Loeve expansion. A Latin hypercube Monte Calro simulation coupled with finite difference method for Terzaghi's one dimensional consolidation theory was then used to probabilistic analysis. The results show that the failure probability is smaller when consider spatial variability of $C_v$ than not considered and the failure probability increased when the autocorrelation distance increased. Thus, the uncertainty of soil can be overestimated when spatial variability of soil property is not considered, and therefore, to perform a more accurate probabilistic analysis, spatial variability of soil property needed to be considered.

• Computational Structural Engineering : An International Journal
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• v.1 no.2
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• pp.139-150
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• 2001

An initial approach for the identification of physical causes underlying the spatial coherency of seismic ground motions it presented. The approach relies on the observation that amplitude and phase variability of seismic data recorded over extended areas around the amplitude and phase of a common, coherent component are correlated. It suffices then to examine the physical causes for the amplitude variability in the seismic motions, in order to recognize the causes for the phase variability and, consequently, the spatial coherency. In this study, the effect of randomness in the shear wave velocity at a site on the amplitude variability of the surface motions mi investigated by means of simulations. The amplitude variability of the simulated motions around the amplitude of the common component is contained within envelope functions, the shape of which suggests, on a preliminary basis, the trend of the decay of coherency with frequency.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.49-60
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• 2021

BCSA (Bias-Correction and Stochastic Analog) is a statistical downscaling technique designed to effectively correct the systematic errors of GCM (General Circulation Model) output and reproduce basic statistics and spatial variability of the observed precipitation filed. In this study, the applicability of BCSA was evaluated using the ASOS observation data over South Korea, which belongs to the monsoon climatic zone with large spatial variability of rainfall and different rainfall characteristics. The results presented the reproducibility of temporal and spatial variability of daily precipitation in various manners. As a result of comparing the spatial correlation with the observation data, it was found that the reproducibility of various climate indices including the average spatial correlation (variability) of rainfall events in South Korea was superior to the raw GCM output. In addition, the needs of future related studies to improve BCSA, such as supplementing algorithms to reduce calculation time, enhancing reproducibility of temporal rainfall patterns, and evaluating applicability to other meteorological factors, were pointed out. The results of this study can be used as the logical background for applying BCSA for reproducing spatial details of the rainfall characteristic over the Korean Peninsula.

• Journal of the Korean Geotechnical Society
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• v.33 no.5
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• pp.25-35
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• 2017

Liquefaction is one of the major seismic damage, and several methods have been developed to evaluate the possibility of liquefaction. Recently, a probabilistic approach has been studied to overcome the drawback of deterministic approaches, and to consider the uncertainties of soil properties. In this study, the spatial variability of cone penetration resistance was evaluated using CPT data from three locations having different variability characteristics to perform the probabilistic analysis considering the spatial variability of soil properties. Then the random fields of cone penetration resistance considering the spatial variability of each point were generated, and a probabilistic analysis of liquefaction induced settlement was carried out through CPT-based liquefaction evaluation method. As a result, the uncertainty of soil properties can be overestimated when the spatial variability is not considered, and significant probabilistic differences can occur up to about 30% depending on the allowable settlement.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.434-436
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• 2005

Analysis method using remote sensing data is one of the effective ways to research a spatial and temporal variability of the mesoscale oceanic motions. During past several decades, many researchers have been getting comprehensive results using remote sensing data with application of data fusion methods in many parts of geo-science. For this study, we took the integration and fusion of several remote sensing data, which are different data resolution, timescale and characteristics, for improving accurate analysis of variation of the Kuroshio Extension. Furthermore, we might get advanced ways to understand the variability of the Kuroshio Extension, has close relation to the spatial and temporal variation of the Kuroshio and Oyashio Current.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1993.10a
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• pp.207-215
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• 1993

With the aid of finite element method, this paper deals with the problem of structural response variability of transmission tower subjected to the spatial variability of material properties, Young's modulus herein. The spatial variability of material property are modeled as two-dimensional stochastic field which has an isotropic auto-correlation function. Response variability has been computed based on two numerical techniques, such as the Neumann expansion method in conjunction with the Monte Carlo simulation method. The results by these numerical methods are compared with those by the deterministic approach.

• Journal of Korean Society on Water Environment
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• v.37 no.3
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• pp.204-216
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• 2021

It is important to understand the factors influencing the temporal and spatial variability of water quality in order to establish an effective customized management strategy for contaminated aquatic ecosystems. In this study, the spatial diversity of the 5-year (2015 - 2019) average total phosphorus (TP) concentration observed in 40 Total Maximum Daily Loads unit-basins in the Nakdong River watershed was analyzed using 50 predictive variables of watershed characteristics, climate characteristics, land use characteristics, and soil characteristics. Cross-correlation analysis, a two-stage exhaustive search approach, and Bayesian inference were applied to identify predictors that best matched the time-averaged TP. The predictors that were finally identified included watershed altitude, precipitation in fall, precipitation in winter, residential area, public facilities area, paddy field, soil available phosphate, soil magnesium, soil available silicic acid, and soil potassium. Among them, it was found that the most influential factors for the spatial difference of TP were watershed altitude in watershed characteristics, public facilities area in land use characteristics, and soil available silicic acid in soil characteristics. This means that artificial factors have a great influence on the spatial variability of TP. It is expected that the proposed statistical modeling approach can be applied to the identification of major factors affecting the spatial variability of the temporal average state of various water quality parameters.