Importance of Lower-Bound Capacities in the Design of Deep Foundations

Najjar, Shadi; Gilbert, Robert B.

doi:10.1061/(asce)gt.1943-5606.0000044

Cited by 49 publications

(16 citation statements)

References 17 publications

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“…Thus, a uniform distribution of D/B = 25, 30,…,65 was selected for reliability simulations based on the observed values in the database and their distribution. Najjar and Gilbert (2009) illustrated the limitations associated with using random samples that follow continuous distributions to estimate reliability. Although the gamma distribution is constrained to positive values, it can lead to over-sampling at the tail ends of the distribution.…”

Section: Hyperbolic Model Parametersmentioning

confidence: 99%

Serviceability limit state reliability-based design of augered cast-in-place piles in granular soils

Reddy¹,

Stuedlein

2017

Can. Geotech. J.

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This study proposes a reliability-based design (RBD) procedure to evaluate the allowable load for augered cast-in-place (ACIP) piles installed in predominately granular soils based on a prescribed level of reliability at the serviceability limit state (SLS). The ultimate limit state (ULS) ACIP pile-specific design model proposed in the companion paper is incorporated into a bivariate hyperbolic load-displacement model capable of describing the variability in the loaddisplacement relationship for a wide range of pile displacements. Following the approach outlined in the companion paper, distributions with truncated lower-bound capacities were incorporated into the reliability analyses. A lumped load-and resistance factor is calibrated using a suitable performance function and Monte Carlo simulations. The average and conservative 95 percent lower-bound prediction intervals for the calibrated load-and resistance factor resulting from the simulations are provided. Although unaccounted for in past studies, the slenderness ratio was shown to have significant influence on foundation reliability. Because of the low uncertainty in the proposed ULS pile capacity prediction model, the use of a truncated distribution had moderate influence on foundation reliability.Author Keywords: ACIP piles; Reliability; Serviceability limit state; Statistics; Design INTRODUCTIONA suitable foundation design will satisfy the strength limit or ultimate limit state (ULS) as well as the serviceability limit state (SLS), which is often associated with the allowable displacement or angular distortion of a structure. At present, the ULS has received considerably more attention in reliability-based design (RBD); however, the SLS is often the governing criterion for many foundation alternatives (Becker 1996;Wang and Kulhawy 2008; Zhang et al. 2008;Uzielli and Mayne 2011). Phoon and Kulhawy (2008) incorporated the accuracy and uncertainty of the Meyerhof (1976) method for estimating shaft resistance of drilled shafts to make assessments of reliability at the SLS for augered cast-in-place (ACIP) piles. However, the Meyerhof method was originally developed to predict the capacity of driven displacement piles and then modified for use with drilled shafts, which are constructed differently than ACIP piles.Additionally, Phoon and Kulhawy (2008) neglected toe bearing resistance when estimating ACIP pile capacity, resulting in a biased and considerably variable model (Phoon et al. 2006). Phoon et al. (2006) noted that models specific to ACIP piles needed to be developed (Phoon et al. 2006).The goal of this study is to use the ACIP pile-specific ULS design models presented in the companion paper (Reddy and Stuedlein 2016) to investigate reliability-based SLS design of ACIP piles installed in predominately granular soils. Those case histories described in the companion paper characterized with high quality load-displacement (Q-δ) curves were used to investigate foundation reliability at the SLS. First, an approach to link the ULS capacity models ...

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Section: Hyperbolic Model Parametersmentioning

confidence: 99%

Serviceability limit state reliability-based design of augered cast-in-place piles in granular soils

Reddy¹,

Stuedlein

2017

Can. Geotech. J.

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“…Eq. (6) implies that if we know the probability density function (PDF) of j c and calculate j d given i X and E d , we can calculate the fragility of this component [6,7,8]. In Eq.…”

Section: Calculating the Conditional Probability Of Each Configurationmentioning

confidence: 99%

“…Ku et al [6] performed a limited number of Monte Carlo simulations to estimate the probability of mooring system failure with various system properties. The probability of failure of suction caissons in mooring systems was discussed by Najjar and Gilbert [7]. Montes-Iturrizaga et al [8] also analyzed individual mooring lines for their reliability and used a new equation to calculate the probability of failure of the individual lines in the 3 intact system.…”

Section: Introductionmentioning

confidence: 99%

“…of the lateral capacity of the suction caissons was taken to be 0.3 [16]. Both the lateral capacity of the suction caissons and the tensile strength of the mooring line segments were assumed to follow lognormal distributions; however, a lower-bound capacity of 55% the mean capacity was assumed for the suction caissons [16]. Table 5.…”

mentioning

confidence: 99%

“…The capacities of the suction caissons were calculated based on a soil shear strength of 3.8 kPa (80 psf) at the seabed with a linear gradient of 1.57 kPa/m (10 psf/ft) below the seabed; the coefficient of variations (C.O.V.) of the lateral capacity of the suction caissons was taken to be 0.3 [16]. Both the lateral capacity of the suction caissons and the tensile strength of the mooring line segments were assumed to follow lognormal distributions; however, a lower-bound capacity of 55% the mean capacity was assumed for the suction caissons [16].…”

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Progressive reliability method and its application to offshore mooring systems

Mousavi

Gardoni

Maadooliat

2013

Engineering Structures

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Assessing the reliability of complex systems (e.g. structures) is essential for a reliability-based optimal design that balances safety and costs of such systems. This paper proposes the Progressive Reliability Method (PRM) for the quantification of the reliability of complex systems. The proposed method is a closed-form solution for calculating the probability of failure. The new method is flexible to the definition of "failure" (i.e., can consider serviceability and ultimate-strength failures) and uses the rules of probability theory to estimate the failure probability of the system or its components. The method is first discussed in general and then illustrated in two examples, including a case study to find the safest configuration and orientation of a 12-line offshore mooring system. The PRM results are compared with results of a similar assessment based on the Monte Carlo simulations. It is shown in the example of twocomponent that using PRM, the importance of system components to system safety can be quantified and compared as input information for maintenance planning.

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Basics of Probability Theory

Zhang,

Xiao,

et al. 2023

Geotechnical Reliability Analysis

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Importance of Lower-Bound Capacities in the Design of Deep Foundations

Cited by 49 publications

References 17 publications

Serviceability limit state reliability-based design of augered cast-in-place piles in granular soils

Serviceability limit state reliability-based design of augered cast-in-place piles in granular soils

Progressive reliability method and its application to offshore mooring systems

Basics of Probability Theory

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