LRFD Calibration of Simple Soil-Structure Limit States Considering Method Bias and Design Parameter Variability

Bathurst, Richard J.; Javankhoshdel, Sina; Allen, Tony M.

doi:10.1061/(asce)gt.1943-5606.0001735

Cited by 46 publications

(8 citation statements)

References 17 publications

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“…A similar scheme appears in the 2014 edition of the Canadian Highway Bridge Design Code (CAN/CSAS614:2014) that presents different resistance factors depending on the "degree of understanding" (low, typical, high) (Fenton et al 2016) and others (e.g. Paikowsky et al 2004;Bathurst, Javankhoshdel, and Allen 2017). For the first time, we can establish a defensible link between site investigation efforts and the economy of the design, which gives us a fair chance of swaying more businessoriented clients to accept site investigation as an investment rather than a cost (Ching, Phoon, and Yu 2014).…”

Section: Generic Databasesmentioning

confidence: 96%

The story of statistics in geotechnical engineering

Phoon

2019

Georisk: Assessment and Management of Risk for Engineered Syste

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The story of statistics in geotechnical engineering can be traced to Lumb's classical Canadian Geotechnical Journal paper on "The Variability of Natural Soils" published in 1966. In parallel, the story of risk management in geotechnical engineering has progressed from design by prescriptive measures that do not require site-specific data, to more refined estimation of site-specific response using limited data from site investigation as inputs to physical models, to quantitative risk assessment (QRA) requiring considerable data at regional/national scales. In an era where data is recognised as the "new oil", it makes sense for us to lean towards decision making strategies that are more responsive to data, particularly if we have zettabytes coming our way. In fact, we already have a lot of data, but the vast majority is shelved after a project is completed ("dark data"). It does not make sense to reduce one zettabyte to a few bytes describing a single cautious value. It does not make sense to expect big data to be precise and to fit a particular favourite physical model as demanded by the classical deterministic world view. This paper advocates the position that there is value in data of any kind (good or not so good quality, or right or wrong fit to a physical model) and the challenge is for the new generation of researchers to uncover this value by hearing what data have to say for themselves, be it using probabilistic, machine learning, or other data-driven methods including those informed by physics and human experience, and to re-imagine the role of the geotechnical engineer in an immersive environment likely to be imbued by machine intelligence.

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Section: Generic Databasesmentioning

confidence: 96%

The story of statistics in geotechnical engineering

Phoon

2019

Georisk: Assessment and Management of Risk for Engineered Syste

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“…Thus, a brief discussion of these variables is in order. Regarding the first variable, it is noted that in the more recent resistance factors calibration, reliability analysis with different levels of sophistication has been used (e.g., Phoon et al 2003;Paikowsky et al 2004;Fenton et al 2016;Bathurst et al 2017;Javankhoshdela et al 2018). According to Phoon (2017), when dealing with the problems of "known unknowns", reliability theory is very useful in handling complex real-world information including imperfections (scarcity of information or incomplete information).…”

Section: Definition Of the Characteristic Value And Recent Developmentsmentioning

confidence: 99%

“…The LRFD compares the factored resistance to the sum of factored loads as follows (e.g., Bathurst et al 2017;Fenton et al 2008;Fenton et al 2016;Kim and Salgado 2011;Phoon et al 2003;Paikowsky 2004;Roberts and Misra 2007):…”

Section: Load and Resistance Factor Design (Lrfd)mentioning

confidence: 99%

Assessing characteristic value selection methods for design with load and resistance factor design (LRFD) — design robustness perspective

et al. 2019

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An important step in load and resistance factor design (LRFD) is the selection of the characteristic values of uncertain soil parameters, which can be quite subjective despite the simplicity of LRFD. This paper assesses five statistical methods for the selection of characteristic values for design with LRFD, focusing on the design robustness. A framework based on the consideration of safety, cost, and design robustness is proposed for assessing these selection methods. This framework is illustrated with an example, the design of a drilled shaft in sand using LRFD, in which the best overall method for selecting the characteristic values is suggested. The implication of the outcome of this study is quite significant in geotechnical engineering practice, as it provides guidance on the selection of the characteristic values for design with LRFD.

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“…In this paper, different combinations of load and resistance models are used to demonstrate quantitatively the influence of choice of model on reliability index outcomes. Reliability index (␤) is computed using a general closedform solution proposed by Bathurst and Javankhoshdel (2017). Computed outcomes were checked using Monte Carlo (MC) simulation and shown to be the same.…”

Section: Objectives and General Approachmentioning

confidence: 99%

“…This paper builds upon prior related work by the writers and co-workers focused on the collection of load and resistance data from physical measurements of geosynthetic MSE walls under operational conditions, and their components (e.g., Allen et al 2001;Bathurst et al 2011;Huang and Bathurst 2009), the development of new load and resistance models (e.g., Allen and Bathurst 2015; Miyata and Bathurst 2012b; Bathurst et al 2008b), the calibration and statistical evaluation of the accuracy of current and proposed new load and resistance models (e.g., Allen and Bathurst 2015, 2018; Huang and Bathurst 2009), lessons learned from closed-form solutions for the calculation of reliability index for simple linear limit state functions and for LRFD calibration (e.g., Allen et al 2005;Bathurst et al 2008aBathurst et al , 2017Bathurst and Javankhoshdel 2017), the influence of correlations between variables in model formulations and between nominal load and resistance terms that appear in limit state equations in reliability calculations (e.g., Lin and Bathurst 2018), and the notion of level of understanding that has been recently adopted in Canada for LRFD of foundations (e.g., Fenton et al 2016). These earlier works provide the background and data that were required to develop for the first time a practical and rigorous RBD approach for tensile rupture and pullout internal limit states for "geosynthetic MSE walls.…”

Section: Introductionmentioning

confidence: 99%

Reliability-based design of internal limit states for mechanically stabilized earth walls using geosynthetic reinforcement

2019

Self Cite

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This paper demonstrates reliability-based design for tensile rupture and pullout limit states for mechanically stabilized earth (MSE) walls constructed with geosynthetic (geogrid) reinforcement. The general approach considers the accuracy of the load and resistance models that appear in each limit state equation plus uncertainty due to the confidence (level of understanding) of the designer at the time of design. The reliability index is computed using a closed-form solution that is easily implemented in a spreadsheet. The general approach provides a quantitative link between nominal factor of safety, which is familiar in allowable stress design practice, and reliability index used in modern civil engineering reliability-based design practice. A well-documented MSE wall case study is used to demonstrate the general approach and to compare margins of safety using different load and resistance model combinations. A practical outcome from the case study example is the observation that the pullout limit state is much less likely to control design than the ultimate tensile rupture state for walls with continuous reinforcement coverage. The more accurate “simplified stiffness method” that is used to compute tensile loads in the reinforcement under operational conditions is shown to generate a more cost-effective reinforcement option than the less accurate American Association of State Highway and Transportation Officials (AASHTO) simplified method.

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LRFD Calibration of Simple Soil-Structure Limit States Considering Method Bias and Design Parameter Variability

Cited by 46 publications

References 17 publications

The story of statistics in geotechnical engineering

The story of statistics in geotechnical engineering

Assessing characteristic value selection methods for design with load and resistance factor design (LRFD) — design robustness perspective

Reliability-based design of internal limit states for mechanically stabilized earth walls using geosynthetic reinforcement

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