Reliability Analysis of Thaw‐Induced Pore Pressures

Banerjee, Sunirmal; Datta, Bithin

doi:10.1061/(asce)0887-381x(1991)5:3(125)

Cited by 2 publications

(4 citation statements)

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“…The first utilizes the uncertainty within the input parameters to calculate the reliability of FOS (Duncan 2000). The second uses a more in-depth statistical analysis to determine the probability of failure in relation to a critical value (Banerjee and Datta 1991). The third, limit state design, also known as load and resistance factor design (LRFD), applies separate factors to the loading and resistance sides of a calculation to provide a consistent reliability of design (Goble 1999).…”

Section: Reliability Analysis Methodsmentioning

confidence: 99%

“…In order to include both aleatory and epistemic uncertainty, Banerjee and Datta (1991) analyzed the reliability of thaw induced pore pressure calculations using Morgenstern and Nixon's Theory (1971). First-order uncertainty analysis of the calculation method was used to mathematically determine the functions of the total, epistemic, and aleatory uncertainty in the calculation of the excess pore water pressure.…”

Section: Reliability Analysis Methodsmentioning

confidence: 99%

“…As discussed above, methods for the quantification of geotechnical uncertainty have been created (Baecher and Christian 2003, Duncan 2000, Goble 1999Kim and Salgado 2008, Lacasse and Nadim 1996, Whitman 1984 and some quantification of engineering calculations has been done for permafrost analyses (Banerjee and Datta, 1991). Additional work is needed to further quantify uncertainty in permafrost engineering calculations and apply these calculations to a risk analysis framework.…”

Section: Knowledge Gaps and Research Project Objectivementioning

confidence: 99%

See 2 more Smart Citations

Quantitative Risk Analysis of Linear Infrastructure: State of the Practice and Research Plan

Brooks¹,

Doré²,

Locat³

et al. 2015

Cold Regions Engineering 2015

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Qualitative analysis processes are currently used to design and allocate maintenance monies for linear infrastructure on permafrost. The routing and maintenance locations are generally selected using a qualitative process, with probabilities and consequences of each potential failure mode evaluated using a scalar rating system. A quantitative risk assessment utilizes calculated values of failure probability and consequence. In either analysis method, the values of probability and consequence are multiplied to determine a risk importance factor, which is used to rank and analyze each risk. While qualitative analysis methods have been used for many years, there is a push within the engineering community towards probability-based methodologies, quantifying the analysis process. This paper presents a review of probabilistic methods used to analyze geotechnical properties and calculation uncertainty, problems and failure modes of embankment-supported infrastructure on permafrost, and risk analysis methods. This review is an initial step in the development of a tool and methodology for quantitative risk assessment of linear infrastructure on permafrost; a brief discussion of the research plan is also included.

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Section: Reliability Analysis Methodsmentioning

confidence: 99%

Section: Reliability Analysis Methodsmentioning

confidence: 99%

Section: Knowledge Gaps and Research Project Objectivementioning

confidence: 99%

See 1 more Smart Citation

Quantitative Risk Analysis of Linear Infrastructure: State of the Practice and Research Plan

Brooks¹,

Doré²,

Locat³

et al. 2015

Cold Regions Engineering 2015

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“…-[66]. Specifically, for groundwater and seepage for hydraulic structures, most studies have concentrated on stochastic analyses of seepage characteristics based on different realizations of hydraulic conductivity generated from different probability distribution functions (PDF) or sets of means and standard deviations[61] [67]-[72].…”

mentioning

confidence: 99%

An Overview of Recently Developed Coupled Simulation Optimization Approaches for Reliability Based Minimum Cost Design of Water Retaining Structures

Al-Juboori¹,

Datta²

2018

OJOp

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This paper reviews several recently-developed techniques for the minimum-cost optimal design of water-retaining structures (WRSs), which integrate the effects of seepage. These include the incorporation of uncertainty in heterogeneous soil parameter estimates and quantification of reliability. This review is limited to methods based on coupled simulation-optimization (S-O) models. In this context, the design of WRSs is mainly affected by hydraulic design variables such as seepage quantities, which are difficult to determine from closed-form solutions or approximation theories. An S-O model is built by integrating numerical seepage modeling responses to an optimization algorithm based on efficient surrogate models. The surrogate models (meta-models) are trained on simulated data obtained from finite element numerical code solutions. The proposed methodology is applied using several machine learning techniques and optimization solvers to optimize the design of WRS by incorporating different design variables and boundary conditions. Additionally, the effects of several scenarios of flow domain hydraulic conductivity are integrated into the S-O model. Also, reliability based optimum design concepts are incorporated in the S-O model to quantify uncertainty in seepage quantities due to uncertainty in hydraulic conductivity estimates. We can conclude that the S-O model can efficiently optimize WRS designs. The ANN, SVM, and GPR machine learning technique-based surrogate models are efficiently and expeditiously incorporated into the S-O models to imitate the numerical responses of simulations of various problems.

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Reliability Analysis of Thaw‐Induced Pore Pressures

Cited by 2 publications

References 12 publications

Quantitative Risk Analysis of Linear Infrastructure: State of the Practice and Research Plan

Quantitative Risk Analysis of Linear Infrastructure: State of the Practice and Research Plan

An Overview of Recently Developed Coupled Simulation Optimization Approaches for Reliability Based Minimum Cost Design of Water Retaining Structures

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