Reliability-Based Serviceability Limit State Design of Spread Footings on Aggregate Pier Reinforced Clay

Huffman, Jonathan C.; Stuedlein, Armin W.

doi:10.1061/(asce)gt.1943-5606.0001156

Cited by 43 publications

(17 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was demonstrated that this approach can be easily used in conjunction with a random allowable settlement (Stuedlein and Reddy 2013, Huffman and Stuedlein 2014, Huffman et al 2015, and Reddy and Stuedlein 2017b). …”

Section: Bivariate Load-settlement Modelmentioning

confidence: 99%

“…Applications of the load-settlement model factors for RBD of foundations at the SLS were presented in Wang and Kulhawy (2008), Uzielli and Mayne (2011), Stuedlein and Reddy (2013), Huffman and Stuedlein (2014), Huffman et al (2015Huffman et al ( , 2016, and Reddy and Stuedlein (2017b processes of a DCIS pile is described as follows (Flynn and McCabe 2016): (1) place a hollow steel tube with a sacrificial circular steel plate at the base of the tube preventing soil and/or water from entering it; (2) place the reinforcement and pour concrete into the tube, when the tube reaches the required depth; and (3) extract the tube from the soil with the steel plate remaining at the base when concreting is complete.…”

Section: Bivariate Load-settlement Modelmentioning

confidence: 99%

“…Kulhawy (2008), Dithinde et al (2011), Huffman andStuedlein (2014), Reddy and Stuedlein (2017b), and Tang and Phoon (2018a, b), etc. It can be explained as follows: a small initial slope of the loadsettlement curve (i.e.…”

Section: Hyperbolic Parameters Simulationmentioning

confidence: 99%

“…Statistics of the load-settlement model factors were reported in Uzielli and Mayne (2011), Huffman and Stuedlein (2014), Huffman et al (2015), and Tang et al (2017a) for spread footings; Stuedlein and Reddy (2013), and Reddy and Stuedlein (2017b) for ACIP piles; Dithinde et al (2011) for drilled shafts; Tang and Phoon (2018a) for steel H-piles; Dithinde et al (2011) for driven concrete or steel pipe piles; and Tang and Phoon (2018b) for torque-driven helical piles. Applications of the load-settlement model factors for RBD of foundations at the SLS were presented in Wang and Kulhawy (2008), Uzielli and Mayne (2011), Stuedlein and Reddy (2013), Huffman and Stuedlein (2014), Huffman et al (2015Huffman et al ( , 2016, and Reddy and Stuedlein (2017b processes of a DCIS pile is described as follows (Flynn and McCabe 2016): (1) place a hollow steel tube with a sacrificial circular steel plate at the base of the tube preventing soil and/or water from entering it; (2) place the reinforcement and pour concrete into the tube, when the tube reaches the required depth; and (3) extract the tube from the soil with the steel plate remaining at the base when concreting is complete.…”

Section: Bivariate Load-settlement Modelmentioning

confidence: 99%

“…The uncertainty in the entire load-displacement curve is represented by a bivariate random vector containing the bivariate load-settlement model factors as its components. Applications of this approach for RBD at the SLS can be found in Huffman and Stuedlein (2014), Huffman et al (2015), Phoon and Kulhawy (2008), Reddy and Stuedlein (2017b), Stuedlein and Reddy (2013), Uzielli and Mayne (2011), and Wang and Kulhawy (2008).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Statistics of model factors in reliability-based design of axially loaded driven piles in sand

Tang

Phoon

2018

Can. Geotech. J.

View full text Add to dashboard Cite

This paper compiles 162 reliable field load tests for axially loaded driven piles in sand from previous studies. The L 1 -L 2 method is adopted to interpret the measured resistance from the load-settlement data. The accuracy of resistance calculations with the ICP-05 and UWA-05 methods based on cone penetration test profile is evaluated by the ratio (bias or model factor) of the measured resistance to the calculated resistance. A hyperbolic model with two parameters, where the load component is normalized by the measured resistance, is utilized to fit the measured load-settlement curves. The means, coefficients of variation, and probability distributions for the resistance model factor and the hyperbolic parameters are established from the database.Copula theory is employed to characterize the correlation structure within the hyperbolic parameters. The statistical properties of the model factors are applied to calibrate the resistance factors in simplified reliabilitybased designs of closed-end piles driven into sand at the ultimate and serviceability limit state by Monte Carlo simulations. A simple example is provided to illustrate the application of the proposed resistance factors to estimate the allowable load for an allowable settlement at the desired serviceability limit probability.

show abstract

Section: Bivariate Load-settlement Modelmentioning

confidence: 99%

Section: Bivariate Load-settlement Modelmentioning

confidence: 99%

Section: Hyperbolic Parameters Simulationmentioning

confidence: 99%

Section: Bivariate Load-settlement Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Statistics of model factors in reliability-based design of axially loaded driven piles in sand

Tang

Phoon

2018

Can. Geotech. J.

View full text Add to dashboard Cite

show abstract

Copula‐based joint probability function for PGA and CAV: a case study from Taiwan

Tang

Wang

et al. 2016

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

Summary This study aims to develop a joint probability function of peak ground acceleration (PGA) and cumulative absolute velocity (CAV) for the strong ground motion data from Taiwan. First, a total of 40,385 earthquake time histories are collected from the Taiwan Strong Motion Instrumentation Program. Then, the copula approach is introduced and applied to model the joint probability distribution of PGA and CAV. Finally, the correlation results using the PGA‐CAV empirical data and the normalized residuals are compared. The results indicate that there exists a strong positive correlation between PGA and CAV. For both the PGA and CAV empirical data and the normalized residuals, the multivariate lognormal distribution composed of two lognormal marginal distributions and the Gaussian copula provides adequate characterization of the PGA‐CAV joint distribution observed in Taiwan. This finding demonstrates the validity of the conventional two‐step approach for developing empirical ground motion prediction equations (GMPEs) of multiple ground motion parameters from the copula viewpoint. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Bayesian assessment of site‐specific performance of geotechnical design charts with unknown model uncertainty

Wang

Zhao

2016

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

Soil properties are indispensable input parameters in geotechnical design and analysis. In engineering practice, particularly for projects with relatively small or medium sizes, soil properties are often not measured directly, but estimated from geotechnical design charts using results of some commonly used laboratory or in situ tests. For example, effective friction angle ϕ′ of soil is frequently estimated using standard penetration test (SPT) N values and design charts relating SPT N values to ϕ′. Note that directly measured ϕ′ data are generally not available when (and probably why) the use of design charts is needed. Because design charts are usually developed from past observation data, on either empirical or semi-theoretical basis, uncertainty is unavoidably involved in the design charts. This situation leads to two important questions in engineering practice: (1) how good or reliable are the soil properties estimated in a specific site when using the design charts? (or how to measure the performance of the design charts in a specific site?); and (2) how to incorporate rationally the model uncertainty when estimating soil properties using the design charts? This paper aims to address these two questions by developing a Bayesian statistical approach. In this paper, the second question is firstly addressed (i.e., soil properties are probabilistically characterized by rationally incorporating the model uncertainty in the design chart). Then, based on the characterization results obtained, an index is proposed to evaluate the site-specific performance of design charts (i.e., to address the first question). Equations are derived for the proposed approach, and the proposed approach is illustrated using both real and simulated SPT data.

show abstract

Reliability-Based Serviceability Limit State Design of Spread Footings on Aggregate Pier Reinforced Clay

Cited by 43 publications

References 19 publications

Statistics of model factors in reliability-based design of axially loaded driven piles in sand

Statistics of model factors in reliability-based design of axially loaded driven piles in sand

Copula‐based joint probability function for PGA and CAV: a case study from Taiwan

Bayesian assessment of site‐specific performance of geotechnical design charts with unknown model uncertainty

Contact Info

Product

Resources

About