Reliability Analysis and Updating of Excavation-Induced Ground Settlement for Building Serviceability Assessment

Hsiao, Evan C. L.; Schuster, Matt; Juang, C. Hsein; Kung, Gordon Tung-Chin

doi:10.1061/(asce)1090-0241(2008)134:10(1448)

Cited by 96 publications

(45 citation statements)

References 26 publications

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“…It is expected that the uncertainty impact is more profound for the parameter that has a predominant effect on the computed excavation responses. For excavation in clays, Hsiao et al (2008) reported that with good workmanship, the soil strength ( ' uc v s σ ) and stiffness ( ' i v E σ ) of clays had the most influence on the excavationinduced wall and ground deformations. Thus, in this paper these two soil parameters are considered as an example to demonstrate the propagation of uncertainty through the FEM approach.…”

Section: Adopted Fem Solution and Variable Input Parametersmentioning

confidence: 99%

“…In this regard, the variation of soil stiffness and strength at TNEC site was obtained through small strain triaxial tests by Kung (2003). These data are shown in Table 1 s σ is equal to 0.3 as suggested by Hsiao et al (2008). These values of computed mean and standard deviation are used in steps 1, 2, and 3 of the aforementioned simplified procedure.…”

Section: Overview Of the Case History And Input Parametersmentioning

confidence: 99%

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Evaluation of Soil Variability Influence on Deep Excavation Analysis–Simplified Approach

2012

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A simplified procedure is developed to provide a tool for assessing the variation of the computed excavation responses, including wall and ground deformations, induced by input parameter uncertainties through a finite element solution. A well-documented case history is employed for demonstration. The proposed scheme appears accurate and easy-to-use in the propagation of parameter uncertainties. Moreover, this procedure is capable of assessing the variability effect of either a single or multiple input parameters. Further, the sensitivity of the uncertainty effect on the excavation characteristics is also examined. The proposed procedure enables a valuable insight regarding how much variation of wall and ground movements that can be expected as a result of parameter uncertainties.

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Section: Adopted Fem Solution and Variable Input Parametersmentioning

confidence: 99%

Section: Overview Of the Case History And Input Parametersmentioning

confidence: 99%

Evaluation of Soil Variability Influence on Deep Excavation Analysis–Simplified Approach

2012

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“…Mainly included in the discussion are (1) methods for model uncertainty evaluation and model calibration which have been recently summarized by Dithinde et al [11] and (2) an overview of existing work of model uncertainty characterization for different geotechnical models in the literature (e.g., shallow and deep foundations [12][13][14][15][16][17] and retaining structures [18][19][20][21]). The objective of this study is to evaluate the model uncertainty of the default FHWA simplified nail load equation using the lower and upper bound nail load data reported by Wentworth [6] and Banerjee et al [7,8].…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation of Model Uncertainty in Predicting Soil Nail Loads Using Default and Modified FHWA Simplified Methods

Liu

Tang

Lin

2017

Mathematical Problems in Engineering

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Accuracy evaluation of the default Federal Highway Administration (FHWA) simplified equation for prediction of maximum soil nail loads under working conditions is presented in this study using the maximum likelihood method and a large amount of measured lower and upper bound nail load data reported in the literature. Accuracy was quantitatively expressed as model bias where model bias is defined as the ratio of measured to predicted nail load. The maximum likelihood estimation was carried out assuming normal and lognormal distributions of bias. Analysis outcomes showed that, based on the collected data, the default FHWA simplified nail load equation is satisfactorily accurate on average and the spread in prediction accuracy expressed as the coefficient of variation of bias is about 30%, regardless of the distribution type. Empirical calibrations were proposed to the default FHWA simplified nail load equation for accuracy improvement. The Bayesian Information Criterion was adopted to perform a comparison of suitability between the competing normal and lognormal statistical models that were intended for description of model bias. Example of reliability-based design of soil nail walls against internal pullout limit state of nails is provided in the end to demonstrate the benefit of performing model calibration and using calibrated model for design of soil nails.

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“…Inverse problem resolution is not new since numerous authors have studied it previously [6][7][8][9][10][11][12][13][14][15][16]. Many of those studies [6,[8][9][10][11]14] address the back analysis as an optimization problem (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Many of those studies [6,[8][9][10][11]14] address the back analysis as an optimization problem (i.e. obtaining the set of parameters which minimize an objective function).…”

Section: Introductionmentioning

confidence: 99%

On the use of stochastic spectral methods in deep excavation inverse problems

Cañavate-Grimal

Falcó

Calderón

et al. 2015

Computers & Structures

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Reliability Analysis and Updating of Excavation-Induced Ground Settlement for Building Serviceability Assessment

Cited by 96 publications

References 26 publications

Evaluation of Soil Variability Influence on Deep Excavation Analysis–Simplified Approach

Evaluation of Soil Variability Influence on Deep Excavation Analysis–Simplified Approach

Maximum Likelihood Estimation of Model Uncertainty in Predicting Soil Nail Loads Using Default and Modified FHWA Simplified Methods

On the use of stochastic spectral methods in deep excavation inverse problems

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