Probabilistic analysis of responses of cantilever wall-supported excavations in sands considering vertical spatial variability

Sert, Sedat; Luo, Zhe; Xiao, Junhua; Gong, Wenping; Juang, C. Hsein

doi:10.1016/j.compgeo.2016.02.004

Cited by 51 publications

(17 citation statements)

References 16 publications

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“…This study assumes the cross-correlation coefficient to be unity, and it is possible to incorporate other values of the coefficient, although this would lead to a more sophisticated mathematical formulation. In addition, the peak dilatancy angle is assumed to be equal to φ p − 30°(with minimum value of 0), which is an approximation also adopted by Sert et al (2016). The soil-wall interface is assumed to have a constant friction angle of 24.5°, which roughly corresponds to interface reduction factor of 0.65 and is in line with the recommendations of local design guidelines.…”

Section: Modeling Of Soil Variabilitymentioning

confidence: 99%

“…This may be attributed to the computational demands associated with modeling of soil spatial variability, which can be exacerbated when incorporated into an updating framework, such as the updating of posterior probability for random field parameters. Nonetheless, probabilistic analyses in recent studies (e.g., Sert et al 2016;Yáñez-Godoy et al 2017) have shown that spatial variability can have significant implications on the response of retaining structures, although there has been limited discussion on the integration of random field theories into the updating framework for improved predictions of system response. Lo and Leung (2016) presented a Bayesian approach to update spatial variability parameters for soils below building foundations, but their approach required a large number of model simulations.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian updating of subsurface spatial variability for improved prediction of braced excavation response

Leung

2019

Can. Geotech. J.

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This paper introduces an approach that utilizes field measurements to update the parameters characterizing spatial variability of soil properties and model bias, leading to refined predictions for subsequent construction stages. It incorporates random field simulations and a surrogate modeling technique into the Bayesian updating framework, while the spatial and stage-dependent correlations of model bias can also be considered. The approach is illustrated using two cases of multi-stage braced excavations, one being a hypothetical scenario and the other from a case study in Hong Kong. Making use of all the deflection measurements along an inclinometer, the principal components of the random field and model bias factors can be updated efficiently as the instrumentation data become available. These various sources of uncertainty do not only cause discrepancies between prior predictions and actual performance, but can also lead to response mechanisms that cannot be captured by deterministic approaches, such as distortion of the wall along the longitudinal direction of the excavation. The proposed approach addresses these issues in an efficient manner, producing prediction intervals that reasonably encapsulate the response uncertainty as shown in the two cases. The capability to continuously refine the response estimates and prediction intervals can help support the decision-making process as the construction progresses.

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Section: Modeling Of Soil Variabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian updating of subsurface spatial variability for improved prediction of braced excavation response

Leung

2019

Can. Geotech. J.

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“…The method utilizes the statistics of the basic independent random variables, such as (R) and (S). If the probability density functions (PDF) and the cumulative distribution function (CPF) of (Z) are defined as (fz (Z)) and (Fz (z)), respectively, the liquefaction probability (P L ) then equals the probability of (Z〈0) (Sert et al 2016). If the mean values and standard deviations of (R) and (S) are (μ R ), (μ S ), (σ R ), (σ S ) according to the first order and second moment method, the mean, standard deviation and coefficient of variance of the (Z) function can be obtained by using the following equations.…”

Section: Methodology Of Reliability Probabilistic Modelmentioning

confidence: 99%

Liquefaction maps in Babol City, Iran through probabilistic and deterministic approaches

Naghizadehrokni

Choobbasti

Naghizadehrokni

2018

Geoenviron Disasters

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Background: During an earthquake, significant damage can result due to instability of the soil in the area affected by internal seismic waves. Liquefaction is known as one of the major causes of ground failure due to the earthquake. Various procedures have been classified for assessing liquefaction phenomenon into two main groups, including the deterministic and probabilistic approaches. Results: Four deterministic methods and one probabilistic approach, which is a reliability procedure are considered for assessing the liquefaction potential in Babol City. The main purpose of this comprehensive research is to evaluate the liquefaction potential and to determine the validation and accuracy of the reliability approach. For this purpose, 60 boreholes including almost 600 field records in different parts of Babol City are analyzed and liquefaction and nonliquefaction areas are identified. Microzonation maps are provided by result analysis of the deterministic and probabilistic procedures. Finally, a 2D borderline, including (CSR) and (N spt ) is obtained by analyzing all data. Conclusions: The present study illustrates that the evaluation of liquefaction potential by using reliability approach is accurate and this procedure can be recognized as one of the best methods for assessing liquefaction. The map obtained utilizing a reliability approach and the borderline provided in this study, can be utilized for recognizing liquefaction and non-liquefaction areas based on different safety factor and probabilistic procedures.

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“…2010, Sert ve Önalp 2011, Bildik vd. 2012, Phien-wej et al 2012, Sert et al 2016, HsiungandYang 2017. Diğer yandan bu amaçla farklı sonlu eleman yazılımlarının kullanıldığı parametrik çalışmalar da literatürde mevcuttur (Gazetas et al 2004, Çakır 2013, Çakır 2014a, Çakır 2014b, Çakır ve Dağ 2015.…”

Section: Introductionunclassified

A ParametricStudyforLateralDisplacement of CantileverDiaphragmWalls

Aslan¹,

Yağci²

2018

AKU-J. Sci. Eng.

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Bu çalışmada, Geo5 programı ile farklı kazı derinliklerindeki konsol diyafram duvarlar için; geoteknik modelde arazi deneylerine dayalı farklı ampirik ilişkilerin ve program kapsamında seçilen farklı zemin yatak katsayısı ilişkilerinin etkisini değerlendiren parametrik bir çalışma yapılmıştır. Sonuçlar doğrultusunda tasarlanan sistemlerin bir bölümü için Plaxis programı ile deformasyon analizleri yapılmış ve maksimum yatay deplasman cinsinden sonuçlar karşılaştırılmıştır. Analizler, sonlu elemanlar yöntemi dışındaki daha basit analiz yöntemlerinde, özellikle seçilen zemin yatak katsayısı yönteminin, sonuçları önemli oranda etkilediğini göstermektedir.

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Probabilistic analysis of responses of cantilever wall-supported excavations in sands considering vertical spatial variability

Cited by 51 publications

References 16 publications

Bayesian updating of subsurface spatial variability for improved prediction of braced excavation response

Bayesian updating of subsurface spatial variability for improved prediction of braced excavation response

Liquefaction maps in Babol City, Iran through probabilistic and deterministic approaches

A ParametricStudyforLateralDisplacement of CantileverDiaphragmWalls

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