Chapter 7 Direct probability-based design methods

Wang, Yu; Schweckendiek, T.; Gong, Wenping; Zhao, Tengyuan; Phoon, Kok‐Kwang

doi:10.1201/9781315364179-8

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…(3) Full RBD (Wang et al 2016) When sufficient data are available, the application of full RBD accounting for all the true uncertainties involved should be encouraged.…”

Section: Discussionmentioning

confidence: 99%

What is a characteristic value for soils?

Länsivaara

Phoon

Ching

2021

Georisk: Assessment and Management of Risk for Engineered Syste

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The choice of the characteristic value of a material property is fundamental in all civil engineering design and critical in geotechnical engineering. Based on our tradition, we strive to look for a cautious value, whose evaluation includes our engineering judgement. While the definition of a characteristic value is often given with the aid of statistics as a fractile value, its determination includes considerations of mechanics because "value" refers to the relevant quantity affecting the limit state. Soils are complex, often spatially heterogeneous and our knowledge of them is typically based on sparse incomplete data. This makes the definition and determination of a characteristic value for soils more multifaceted than for structural materials. This paper discusses the choice of the characteristic value for soils from various perspectives (statistical, mechanistic and practical) and summarises some recent findings to clarify the limitations of existing practice. Overly simplified statistical approaches may lead to unsafe or overly conservative designs. The authors conclude that a balance between practicality and incorporating salient features are needed. By increasing the value of data in design decisions, engineers will be motivated to collect, share, and utilise data as much as possible and bring our practice closer to the digital economy.

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“…(3) Full RBD (Wang et al 2016) When sufficient data are available, the application of full RBD accounting for all the true uncertainties involved should be encouraged.…”

Section: Discussionmentioning

confidence: 99%

What is a characteristic value for soils?

Länsivaara

Phoon

Ching

2021

Georisk: Assessment and Management of Risk for Engineered Syste

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“…The Latin Hypercubic sampling technique was implemented for efficient sampling. The outcomes of the Monte Carlo simulation were statistically analyzed to estimate pf and p(D/H, α, h1/H |Failure) as [14].…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

“…The application of reliability-based design methods relies on the knowledge of the statistical data of soil design parameters that have a significant effect on the stability of earth retaining structures [13], [14]. In an uncertainty analysis framework, uncertain engineering quantities (i.e., soil strength) are modeled by random variables through stochastic models (e.g., FORM, Monte Carlo simulation, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Reliability Analysis of Bored-pile Wall Stability Considering Parameter Uncertainties

Mattos

Marin

2020

TecnoL.

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In geotechnical engineering, bored-pile wall stability is evaluated using deterministic design methods based on safety factors to establish a margin against failure. In recent years, reliability-based design methods have been adopted to include uncertainty in the assessment of bored-pile wall stability as well as in the calculation of the feasible embedment depth of the walls. In this study, an expanded reliability-based design approach, along with finite element analysis, was applied to conduct parametric analyses of bored-pile wall stability. In serviceability limit state design framework, the results indicate that cohesion and groundwater level are factors that significantly affect bored-pile wall stability. Moreover, high variability in the cohesion range causes great uncertainty to determine the embedment depth of bored-pile wall. The feasible embedment depth can reach 4 times the free height considering the maximum coefficient of variation (50 %) of the cohesion. In turn, when the groundwater level is located at the retained ground surface, the horizontal displacement of the upper end of the wall reaches 15.2 mm, i.e., 0.0038 times the free height of the wall, for which the soil mobilizes active earth pressures. It was also found that the resolution of probabilistic results is highly influenced by the number of iterations in Monte Carlo simulations.

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“…Such an observation emphasizes the importance of data in geotechnical engineering. The full value of a database is arguably best realized within a direct probability-based design method (Wang et al 2016). Recent development in the gradual adoption of RBD is more sensitive to information.…”

Section: Database Of Pile Load Testsmentioning

confidence: 99%

Characterization of model uncertainty in predicting axial resistance of piles driven into clay

Tang

Phoon

2019

Can. Geotech. J.

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This paper summarizes 239 static load tests to evaluate the performance of four static design methods for axial resistance of driven piles in clay. The methods are ISO 19901-4:2016, SHANSEP, ICP-05, and NGI-05. The database is categorized into four groups depending on the load type (compression or uplift) and pile tip condition (open or closed end). The model uncertainty in resistance prediction is quantified as a ratio between measured and calculated resistance, which is called a model factor. The measured resistance is interpreted as a load producing a settlement level of 10% pile diameter. Database studies show that the four methods present a similar accuracy, where the mean and coefficient of variation (COV) of the model factor are around 1 and 0.3, respectively. The COV values are smaller than those for driven piles in sand available in literature. The model statistics determined from the database are applicable to a simplified or full probabilistic form of reliability-based design (RBD) of driven piles in clay. As an illustration, the resistance factors in load and resistance factor design (LRFD, a simplified form of RBD) are calibrated by Monte Carlo simulations.

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Chapter 7 Direct probability-based design methods

Cited by 9 publications

References 38 publications

What is a characteristic value for soils?

What is a characteristic value for soils?

Reliability Analysis of Bored-pile Wall Stability Considering Parameter Uncertainties

Characterization of model uncertainty in predicting axial resistance of piles driven into clay

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