Implementing statistical fitting and reliability analysis for geotechnical engineering problems in R

Wu, Xing Zheng

doi:10.1080/17499518.2016.1201577

Cited by 15 publications

(14 citation statements)

References 31 publications

(56 reference statements)

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“…We should also mention that the geotechnical example (Section 6) was very convenient in the sense that the number of relevant variables was two (cohesion and friction angle) and we merely switched from the bivariate modeling of stress ( X 1 ) and strength ( X 2 ) to modeling of cohesion ( Y 1 ) and friction angle ( Y 2 ). However, as stated in Section 1, X 1 and X 2 may be functions of more than two variables such as in Wu 38 where four soil characteristics (including cohesion and friction angle) are considered. X 1 and X 2 are nonlinear functions of those four variables, and the reliability is obtained from the factor of safety, F = X 2 / X 1 , by geometrical methods such as FORM or just descriptively,

{\overset{x}{true}}_{2} false/ {\overset{x}{true}}_{1}

.…”

Section: Discussionmentioning

confidence: 99%

“…In geotechnical engineering, slope stability analysis has been a fertile ground for probabilistic reliability calculations including the use of copula-based models. 36,38 In such calculations, there are two key variables associated with the Mohr-Coulomb failure criterion: the cohesion Y 1 and the angle of internal friction Y 2 . These two soil shear strength features are paired measurements and are statistically dependent.…”

Section: Geotechnical Slope Stability Analysismentioning

confidence: 99%

“…In geotechnical engineering, slope stability analysis has been a fertile ground for probabilistic reliability calculations including the use of copula‐based models 36,38 …”

Section: Geotechnical Slope Stability Analysismentioning

confidence: 99%

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Parametric and nonparametric inference for the reliability of copula‐based stress‐strength models

Andrade

Nascimento²,

Rathie

2020

Quality & Reliability Eng

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This paper provides a general treatment of statistical inference for the reliability in copula-based stress-strength models. Most of the current literature is either focused on specific models that yield clean formulas or restricted to estimation and engineering aspects without addressing statistical inference. We present two general frameworks, one parametric, one nonparametric, for the estimation of the reliability. The parametric methodology is presented under the general framework of estimating equations, mostly as a combination of existing methodologies from the fields of multivariate analysis, reliability, and econometrics, with some new results. The nonparametric methodology is a novel application based on an existing bivariate kernel method combined with Monte Carlo estimation of the reliability without specification of the copula or the margins. We present results from a small simulation study designed to assess the robustness of the methods discussed in terms of model misspecification. We used geotechnical data and data from the Brazilian Household Survey to illustrate the proposed methodologies in the estimation of factors of safety and financial fragility.

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{\overset{x}{true}}_{2} false/ {\overset{x}{true}}_{1}

.…”

Section: Discussionmentioning

confidence: 99%

Section: Geotechnical Slope Stability Analysismentioning

confidence: 99%

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Parametric and nonparametric inference for the reliability of copula‐based stress‐strength models

Andrade

Nascimento²,

Rathie

2020

Quality & Reliability Eng

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“…Currently, more than 10500 packages for different tasks and calculations exist. The R statistical environment is very appropriate for the Geological Sciences (e.g., Grunsky, 2002b;Janoušek et al, 2006Janoušek et al, , 2016Pebesma et al 2012;Wu, 2016). Particularly mentionable is the GeoChemical Data toolkit (GCDkit, Janoušek et al, 2006Janoušek et al, , 2016.…”

Section: Statistics Data Analysis and Visualizationmentioning

confidence: 99%

Using Free/Libre and Open Source Software in the Geological Sciences

D¹,

Schenk²

2017

AJES

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In the Geological Sciences, as in any other academic field, computers and software aided work are essential tools. Although Free and Open Source software is largely used in academic institutions for several purposes it is not yet state-of-the-art for the everyday usage. The usage of free and open source software is, besides the freedom of its ease of use, distribution, and modification, also recommended due to the increasing financial burden. There are many suited and effective alternative free software applications to the most common used proprietary commercial ones. Many common work steps can even be done entirely with the free operating system Linux. A selection of free software applications is compiled which are useful for geoscientific data evaluation and presentation. The provided information aims to lower the threshold of reservations against a potential migration and gives an overview about currently available alternative software useful in the Geological Sciences.In den geologischen Wissenschaften, wie auch in allen anderen Disziplinen, sind Computer, sowie softwareunterstützte Tätig-keiten, längst unverzichtbare Werkzeuge. Obgleich Freie und Open Source Software in vielen speziellen Einsatzgebieten verbreitet ist, wird diese noch nicht umfassend für den täglichen Gebrauch benutzt. Die Verwendung von Freier und Open Source Software ist, neben der Freiheit sie uneingeschränkt nutzen, verteilen und modifizieren zu können, auch wegen zunehmender budgetärer Einschränkungen zu empfehlen. Mit diesem Artikel möchten wir die geologische Gemeinschaft auf die vielen geeigneten Programme hinweisen, die als Alternativen zu den üblicherweise häufig benutzten proprietären, kommerziellen Programmen existieren. Viele alltägliche computergestützte Aufgaben können auch zur Gänze mit dem freien Betriebsystem Linux durchgeführt werden, ohne Einbußen in Funktionalität oder Professionalität befürchten zu müssen. Eine ausgewählte Liste geowissenchaftlich interessanter freier Anwendungen wird durch Referenzen ergänzt. Dieser Artikel soll über die Möglichkeiten freier alternativer Software informieren, die Zurückhaltung vor einer eventuellen Migration mindern, sowie einen Überblick über die aktuell verfügbare freie geowissenschaftliche Software geben.

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“…In recent years, due to the dramatic changes in the climate, environmental changes have gradually increased, landslide analysis of the external and internal environmental parameters has increased the uncertainty [11], and more and more scholars of slope stability analysis and research use probabilistic techniques [12][13][14][15][16][17][18][19]. Probability analysis is applied to slope stability analysis, collecting relevant literature, often using methods as follows: (1) First and Second Order Reliability Methods (FORM and SORM) or First Order Second Moment Method (FOSM) [20][21][22]; (2) Response Surface Method (RSM) [23][24][25]; (3) Subset Simulation [13,25,26]; (4) Stochastic Collocation (SC) [27,28]; (5) Quadrature Method of Moments (QMOM) [29,30]; (6) Monte Carlo simulations (MCS) [25,[31][32][33][34][35]; and (7) Point Estimate Method (PEM) [36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Influence of Failure Probability Due to Parameter and Anchor Variance of a Freeway Dip Slope Slide—A Case Study in Taiwan †

Chen

Cheng

2017

Entropy

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Abstract:The traditional slope stability analysis used the Factor of Safety (FS) from the Limit Equilibrium Theory as the determinant. If the FS was greater than 1, it was considered as "safe" and variables or parameters of uncertainty in the analysis model were not considered. The objective of research was to analyze the stability of natural slope, in consideration of characteristics of rock layers and the variability of pre-stressing force. By sensitivity and uncertainty analysis, the result showed the sensitivity for pre-stressing force of rock anchor was significantly smaller than the cohesive (c) of rock layer and the varying influence of the friction angle (φ) in rock layers. In addition, the immersion by water at the natural slope would weaken the rock layers, in which the cohesion c was reduced to 6 kPa and the friction angle φ was decreased below 14 • , and it started to show instability and failure in the balance as FS became smaller than 1. The failure rate to the slope could be as high as 50%. By stabilizing with a rock anchor, the failure rate could be reduced below 3%, greatly improving the stability and the reliability of the slope.

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Implementing statistical fitting and reliability analysis for geotechnical engineering problems in R

Cited by 15 publications

References 31 publications

Parametric and nonparametric inference for the reliability of copula‐based stress‐strength models

Parametric and nonparametric inference for the reliability of copula‐based stress‐strength models

Using Free/Libre and Open Source Software in the Geological Sciences

Influence of Failure Probability Due to Parameter and Anchor Variance of a Freeway Dip Slope Slide—A Case Study in Taiwan †

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