Robust Geotechnical Design of Drilled Shafts in Sand: New Design Perspective

Juang, C. Hsein; Wang, Lei; Liu, Zhifeng; Ravichandran, Nadarajah; Huang, Hongwei; Zhang, Jie

doi:10.1061/(asce)gt.1943-5606.0000956

Cited by 71 publications

(26 citation statements)

References 27 publications

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“…Although the RBD approach can consider uncertainties explicitly, an accurate statistical characterization of uncertainties of geotechnical parameters is necessary, which is often a challenge in geotechnical practice because of the complexity of natural deposits coupled with limited data availability (Juang et al 2013a, b). Underestimation of the variability in geotechnical parameters may lead to a violation of safety constraints even if the design is conducted using the RBD approach (Juang et al 2013b;Wang et al 2013;Khoshnevisan et al 2014Khoshnevisan et al , 2016.…”

Section: Introductionmentioning

confidence: 99%

“…The reliability-based RGD approach has been applied to the design of foundations (Juang et al 2012;Juang and Wang 2013), drilled shafts (Juang et al 2013b), and rock slopes . In these studies, the geotechnical system performance was analyzed probabilistically and the variation in system performance, in terms of failure probability (P f ), was adopted as a measure of design robustness.…”

Section: Introductionmentioning

confidence: 99%

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Reliability-based robust geotechnical design using Monte Carlo simulation

Peng

Cao

et al. 2016

Bull Eng Geol Environ

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The reliability-based robust geotechnical design (RGD) approach provides an effective tool to deal with the uncertainty in the estimated statistics of geotechnical parameters in a reliability-based design. However, the existing reliability-based RGD approach is not straightforward to apply as it involves multiple concepts. In this paper, the applicability of the existing reliability-based RGD approach is improved by utilizing Monte Carlo simulation (MCS). Here, an MCS-based weighted technique is used to evaluate the failure probability of a geotechnical system. With the aid of this weighted technique, the variation in the failure probability, caused by the uncertainty in the estimated statistics of geotechnical parameters, is computed using MCS. To further improve the efficiency of the RGD method, a series of singleobjective optimizations are used in lieu of a multi-objective optimization in the robust design optimization process. The proposed MCS-based RGD approach is illustrated through an example of rock slope design. Compared with the existing reliability-based RGD approach, the MCS-based RGD approach is not only more intuitive and easier to follow, but also more computationally efficient.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability-based robust geotechnical design using Monte Carlo simulation

Peng

Cao

et al. 2016

Bull Eng Geol Environ

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“…Step 5: The outer loop in Figure 12 that using point estimation method (PEM [48]) for M times is accomplished in this step. Subsequently, the mean and standard deviation of the failure probability are obtained for all designs.…”

Section: Ga and Pso In Foundation Designmentioning

confidence: 99%

“…The deterministic model for ultimate limit state (ULS) and serviceability limit state (SLS) for the system can be counted for probabilistic analysis. ULS and SLS probability may be calculated based on first order reliability method (FORM [48]). …”

Section: Ga and Pso In Foundation Designmentioning

confidence: 99%

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A Review: Evolutionary Computations (GA and PSO) in Geotechnical Engineering

Andrab¹,

Hekmat²,

Yusop³

2017

CWEEE

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This study briefly reviews the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in geotechnical engineering since GA and PSO are widely used in civil engineering. The application of GA and PSO is studied in three popular families of geotechnical problems including unconfined seepage analysis, slope stability analysis, and foundation design. In each category the available results from different studies are reviewed and compared. The comparison of results shows the desirable accuracy in the predicting of optimal values in the process of analysis and design. The presented methods perform successfully in the reviewed problems. However, PSO predicts the optimum values in fewer numbers of iterations, which suggests higher performance in term of implementation/application.

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Multi‐objective reliability‐based robust design for a rock tunnel support system using Pareto optimality

Zhang,

Li,

Zhang

et al. 2024

Num Anal Meth Geomechanics

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In the context of rock material and modeling uncertainties, the optimization of rock tunnel support systems is often conducted by selecting the most cost‐effective solution among several feasible options that typically rely on the engineer's experience, potentially leading to overlooking the most optimal design. To improve such a limitation, this paper presents a multi‐objective reliability‐based robust design, considering the cost, safety, and design robustness systematically while maintaining the computational efficiency. In this framework, the uncertainty‐based reliability constrains is performed using the first‐order reliability method (FORM) and an improved Hasofer–Lind–Rackwits–Fiessler recursive algorithm (iHLRF‐x). The design robustness, in terms of sensitivity index (SI), is evaluated using the normalized gradient of the system response to the noise factors, which can be efficiently obtained from the output of FORM analysis. Then, the Pareto front revealing the tradeoff between multiple objectives can be directly generated using the proposed optimization framework. To illustrate the effectiveness of this procedure, a set of the optimal design combinations of the shotcrete thickness and installation position for the exampled rock tunnel are obtained, and new perspectives pertaining the success of the reliability‐based robust designs are provided.

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Robust Geotechnical Design of Drilled Shafts in Sand: New Design Perspective

Cited by 71 publications

References 27 publications

Reliability-based robust geotechnical design using Monte Carlo simulation

Reliability-based robust geotechnical design using Monte Carlo simulation

A Review: Evolutionary Computations (GA and PSO) in Geotechnical Engineering

Multi‐objective reliability‐based robust design for a rock tunnel support system using Pareto optimality

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