• Earthquakes and Structures
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• v.12 no.2
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• pp.165-175
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• 2017

This paper studies soil properties uncertainty and its implementation in the seismic response evaluation of structures. For this, response sensitivity of two 4- and 12-story RC shear walls to the soil properties uncertainty by considering soil structure interaction (SSI) effects is investigated. Beam on Nonlinear Winkler Foundation (BNWF) model is used for shallow foundation modeling and the uncertainty of soil properties is expanded to the foundation stiffness and strength parameters variability. Monte Carlo (MC) simulation technique is employed for probabilistic evaluations. By investigating the probabilistic evaluation results it's observed that as the soil and foundation become stiffer, the soil uncertainty is found to be less important in influencing the response variability. On the other hand, the soil uncertainty becomes more important as the foundation-structure system is expected to experience nonlinear behavior to more sever degree. Since full This paper studies soil properties uncertainty and its implementation in the seismic response evaluation of structures. For this, response sensitivity of two 4- and 12-story RC shear walls to the soil properties uncertainty by considering soil structure interaction (SSI) effects is investigated. Beam on Nonlinear Winkler Foundation (BNWF) model is used for shallow foundation modeling and the uncertainty of soil properties is expanded to the foundation stiffness and strength parameters variability. Monte Carlo (MC) simulation technique is employed for probabilistic evaluations. By investigating the probabilistic evaluation results it's observed that as the soil and foundation become stiffer, the soil uncertainty is found to be less important in influencing the response variability. On the other hand, the soil uncertainty becomes more important as the foundation-structure system is expected to experience nonlinear behavior to more sever degree. Since full probabilistic analysis methods like MC commonly are very time consuming, the feasibility of simple approximate methods' application including First Order Second Moment (FOSM) method and ASCE41 proposed approach for the soil uncertainty considerations is investigated. By comparing the results of the approximate methods with the results obtained from MC, it's observed that the results of both FOSM and ASCE41 methods are in good agreement with the results of MC simulation technique and they show acceptable accuracy in predicting the response variability.

• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.5
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• pp.125-139
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• 1996

This study was performed to provide the design method for soil structure which guarantees proper safety with uncertainty of soil parameters. For this purpose, the effect of uncertainty of soil parameters for slope stability was analyzed by Bishop's simplified method and Monte Carlo simulation(MC). And reliability analysis program, RESFEM, was developed by combining elastic theory, MC, FEM, SFEM, and reliability, which can consider uncertainty of soil parameters. For factor of safety(FS) 1.0 and 1.2 by Bishop's simplified method, the probability of failure(Pf) was analyzed with varying coefficient of variation(c.o.v.) of soil parameters. The Pf increased as c.o.v. of soil parameters increased. This implies that FS is not the absolute index of slope safety, and even if FS is same, it has different Pf according to c.o.v. of soil parameters. The RESFEM was able to express the Pf at each element in slope quantitatively according to uncertainty of soil parameters. The variation of Pf with uncertainty of soil parameters was analyzed by RESFEM, and it was shown that the Pf increased as the c.o.v. of soil parameters increased.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.77-87
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• 2012

Recently, Load and Resistance Factor Design (LRFD) based on reliability analysis has become a global trend for economical and rational design. In order to implement the LRFD, quantification of uncertainty for load and resistance should be done. The reliability of result relies on input variable, and therefore, it is important to obtain exact uncertainty properties of load and resistance. Since soil stress is the main reason causing the settlement or deformation of ground and load on the underground structure, it is essential to clarify the uncertainty of soil stress distribution for accurately predict the uncertainty of load in LRFD. In this study, laboratory model test on silty sand bed under probabilistic load is performed to observe propagation of upper load uncertainty. The results show that the coefficient of variation (COV) of soil stress are varied depending on location due to non-linear relationship between upper load increment and soil pressure increment. In addition, when the load uncertainty is transmitted through ground, COV is decreased by damping effect.

• Journal of Soil and Groundwater Environment
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• v.18 no.1
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• pp.94-101
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• 2013

It is well known that uncertainty associated with soil sampling is bigger than that with analysis. In this research, uncertainties for soil sampling when assessing TPH and BTEX concentration in soils were quantified based on actual field data. It is almost impossible to assess exact contamination of the site regardless how carefully devised for sampling. Uncertainties associated with sample reduction for further chemical analysis were quantified approximately 10 times larger than those associated with core sampling on site. Bigger uncertainties occur when contamination level is low, sample quantity is small, and soil particle is coarse. To minimize the uncertainties on field, homogenization of soil sample is necessary and its procedures are proposed in this research as well.

• Journal of the Korean Geotechnical Society
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• v.29 no.3
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• pp.41-50
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• 2013

There always exists uncertainty which is mainly due to uncertainty of the evaluation of a geotechnical structure at a site. The uncertainty in the geotechnical analysis can be considered in the probabilistic analysis using the Monte Carlo Simulation. It needs various soil profiles which could be possible at the target site. In this study, a new method is proposed to generate soil profiles which are probable at the site. The proposed method analyzes a structure of a site and generates one dimensional soil profiles for a probabilistic analysis. Through the field application, the applicability of the prosed method was shown.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.123-130
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• 2003

The estimation of key soil properties and subsequent quantitative assessment of the associated uncertainties has always been an important issue in geotechnical engineering. It is well recognized that soil properties vary spatially as a result of depositional and post-depositional processes. The stochastic nature of spatially varying soil properties can be treated as a random field. A practical statistical approach that can be used to systematically model various sources of uncertainty is presented in the context of reliability analysis of slope stability Newly developed expressions for probabilistic characterization of soil properties incorporate sampling and measurement errors, as well as spatial variability and its reduced variance due to spatial averaging. Reliability analyses of the probability of slope failure using the different statistical representations of soil properties show that the incorporation of spatial correlation and conditional simulation leads to significantly lower probability of failure than obtained using simple random variable approach.

• Journal of The Geomorphological Association of Korea
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• v.23 no.3
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• pp.67-78
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• 2016

Soil carbon(C) is an essential property for characterizing soil quality. Understanding spatial patterns of soil C is particularly limited for mountain areas. This study aims to predict the spatial pattern of soil C using terrain analysis in a steep mountainous area. Specifically, model performances and prediction uncertainties were investigated based on the number of resampling repetitions. Further, important predictors for soil C were also identified. Finally, the spatial distribution of uncertainty was analyzed. A total of 91 soil samples were collected via conditioned latin hypercube sampling and a digital soil C map was developed using support vector regression which is one of the powerful machine learning methods. Results showed that there were no distinct differences of model performances depending on the number of repetitions except for 10-fold cross validation. For soil C, elevation and surface curvature were selected as important predictors by recursive feature elimination. Soil C showed higher values in higher elevation and concave slopes. The spatial pattern of soil C might possibly reflect lateral movement of water and materials along the surface configuration of the study area. The higher values of uncertainty in higher elevation and concave slopes might be related to geomorphological characteristics of the research area and the sampling design. This study is believed to provide a better understanding of the relationship between geomorphology and soil C in the mountainous ecosystem.

• Journal of Soil and Groundwater Environment
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• v.19 no.1
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• pp.26-33
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• 2014

Uncertainty associated with a sampling method is very high in evaluating the degree of site contamination; therefore, such uncertainty affects the reliability of precise investigation and remediation verification. In particular, in evaluating a site for a small-sized filling station, underground utilities, such as connection pipes and oil storage tanks, make grid-unit sampling impossible and the resulting increase in uncertainty is inevitable. Accordingly, this study quantified the uncertainty related to the evaluation of the degree of contamination by total petroleum hydrocarbon and by benzene, toluene, ethylene, and xylene. When planning a grid aimed at detecting a hot spot, major factors that influence the increase in uncertainty include grid interval and the size and shape of the hot spot. The current guideline for soil sampling prescribes that the grid interval increase in proportion to the area of the evaluated site, but this heightens the possibility that a hot spot will not be detected. In evaluating a site, therefore, it is crucial to estimate the size and shape of the hot spot in advance and to establish a sampling plan considering a diversity of scenarios.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.597-609
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• 2023

One of the applicable methods for the stabilization of soil walls is the nailing system which consists of tensile struts. The stability and safety of soil nail wall systems are influenced by the geometrical parameters of the nailing system. Generally, the determination of nailing parameters in order to achieve optimal performance of the nailing system for the safety of soil walls is defined in the framework of optimization problems. Also, according to the various uncertainty in the mechanical parameters of soil structures, it is necessary to evaluate the reliability of the system as a probabilistic problem. In this paper, the optimal design of the nailing system is carried out in deterministic and probabilistic cases using meta-heuristic and reliability-based design optimization methods. The colliding body optimization algorithm and first-order reliability method are used for optimization and reliability analysis problems, respectively. The objective function is defined based on the total cost of nails and safety factors and reliability index are selected as constraints. The mechanical properties of the nailing system are selected as design variables and the mechanical properties of the soil are selected as random variables. The results show that the reliability of the optimally designed soil nail system is very sensitive to uncertainty in soil mechanical parameters. Also, the design results are affected by uncertainties in soil mechanical parameters due to the values of safety factors. Reliability-based design optimization results show that a nailing system can be designed for the expected level of reliability and failure probability.

• Earthquakes and Structures
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• v.22 no.3
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• pp.219-230
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• 2022

Knowing that the variability of soil properties is an important source of uncertainty in geotechnical analyses, we will study in this paper the effect of this variability on the seismic response of a structure within the framework of Soil Structure Interaction (SSI). We use the proposed and developed model (N2-ISS, Mekki et al., 2014). This approach is based on an extension of the N2 method by determining the capacity curve of the fixed base system oscillating mainly in the first mode, then modified to obtain the capacity curve of the system on a flexible basis using the concept of the equivalent nonlinear oscillator. The properties of the soil that we are interested in this paper will be the shear wave velocity and the soil damping. These parameters will be modeled at first, as independent random fields, then, the two parameters will be correlated. The results obtained showed the importance of the use of random field in the study of SSI systems. The variability of soil damping and shear wave velocity introduces significant uncertainty not only in the evaluation of the damping of the soil-structure system but also in the estimation of the displacement of the structure and the base-shear force.