Example of Probabilistic Engineering Mechanics format
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Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format
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Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format Example of Probabilistic Engineering Mechanics format
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open access Open Access ISSN: 2668920 e-ISSN: 18784275

Probabilistic Engineering Mechanics — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Nuclear Energy and Engineering #14 of 66 down down by 5 ranks
Aerospace Engineering #30 of 129 down down by 13 ranks
Statistical and Nonlinear Physics #11 of 45 down down by 4 ranks
Ocean Engineering #27 of 96 down down by 16 ranks
Mechanical Engineering #171 of 596 down down by 64 ranks
Civil and Structural Engineering #93 of 318 down down by 48 ranks
Condensed Matter Physics #152 of 411 down down by 45 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 143 Published Papers | 542 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 29/06/2020
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

2.411

4% from 2018

Impact factor for Probabilistic Engineering Mechanics from 2016 - 2019
Year Value
2019 2.411
2018 2.329
2017 1.693
2016 1.714
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 4% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

3.8

30% from 2019

CiteRatio for Probabilistic Engineering Mechanics from 2016 - 2020
Year Value
2020 3.8
2019 5.4
2018 5.0
2017 3.8
2016 3.4
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 30% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

0.884

22% from 2019

SJR for Probabilistic Engineering Mechanics from 2016 - 2020
Year Value
2020 0.884
2019 1.131
2018 1.255
2017 0.89
2016 0.825
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 22% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

1.564

9% from 2019

SNIP for Probabilistic Engineering Mechanics from 2016 - 2020
Year Value
2020 1.564
2019 1.718
2018 1.598
2017 1.466
2016 1.979
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has decreased by 9% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

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Probabilistic Engineering Mechanics

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Elsevier

Probabilistic Engineering Mechanics

This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear...... Read More

Energy

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Last updated on
29 Jun 2020
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ISSN
0266-8920
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Impact Factor
High - 2.102
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
elsarticle-num
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Citation Type
Numbered
[25]
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Bibliography Example
G. E. Blonder, M. Tinkham, T. M. Klapwijk, Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion, Phys. Rev. B 25 (7) (1982) 4515–4532. URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1016/S0266-8920(01)00019-4
Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation
Siu-Kui Au1, James L. Beck1

Abstract:

A new simulation approach, called 'subset simulation', is proposed to compute small failure probabilities encountered in reliability analysis of engineering systems. The basic idea is to express the failure probability as a product of larger conditional failure probabilities by introducing intermediate failure events. With a ... A new simulation approach, called 'subset simulation', is proposed to compute small failure probabilities encountered in reliability analysis of engineering systems. The basic idea is to express the failure probability as a product of larger conditional failure probabilities by introducing intermediate failure events. With a proper choice of the conditional events, the conditional failure probabilities can be made sufficiently large so that they can be estimated by means of simulation with a small number of samples. The original problem of calculating a small failure probability, which is computationally demanding, is reduced to calculating a sequence of conditional probabilities, which can be readily and efficiently estimated by means of simulation. The conditional probabilities cannot be estimated efficiently by a standard Monte Carlo procedure, however, and so a Markov chain Monte Carlo simulation (MCS) technique based on the Metropolis algorithm is presented for their estimation. The proposed method is robust to the number of uncertain parameters and efficient in computing small probabilities. The efficiency of the method is demonstrated by calculating the first-excursion probabilities for a linear oscillator subjected to white noise excitation and for a five-story nonlinear hysteretic shear building under uncertain seismic excitation. read more read less

Topics:

Subset simulation (77%)77% related to the paper, Law of total probability (68%)68% related to the paper, Chain rule (probability) (66%)66% related to the paper, Conditional probability (58%)58% related to the paper, Markov chain Monte Carlo (56%)56% related to the paper
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1,523 Citations
Journal Article DOI: 10.1016/0266-8920(86)90033-0
Multivariate distribution models with prescribed marginals and covariances
Pei-Ling Liu1, A. Der Kiureghian1

Abstract:

Two multivariate distribution models consistent with prescribed marginal distributions and covariances are presented. The models are applicable to arbitrary number of random variables and are particularly suited for engineering applications. Conditions for validity of each model and applicable ranges of correlation coefficien... Two multivariate distribution models consistent with prescribed marginal distributions and covariances are presented. The models are applicable to arbitrary number of random variables and are particularly suited for engineering applications. Conditions for validity of each model and applicable ranges of correlation coefficients between the variables are determined. Formulae are developed which facilitate evaluation of the model parameters in terms of the prescribed marginals and covariances. Potential uses of the two models in engineering are discussed. read more read less

Topics:

Multivariate t-distribution (70%)70% related to the paper, Marginal distribution (70%)70% related to the paper, Joint probability distribution (70%)70% related to the paper, Normal-Wishart distribution (68%)68% related to the paper, Multivariate normal distribution (64%)64% related to the paper
713 Citations
Journal Article DOI: 10.1016/J.PROBENGMECH.2009.10.003
An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis
Géraud Blatman1, Bruno Sudret1

Abstract:

Polynomial chaos (PC) expansions are used in stochastic finite element analysis to represent the random model response by a set of coefficients in a suitable (so-called polynomial chaos) basis. The number of terms to be computed grows dramatically with the size of the input random vector, which makes the computational cost of... Polynomial chaos (PC) expansions are used in stochastic finite element analysis to represent the random model response by a set of coefficients in a suitable (so-called polynomial chaos) basis. The number of terms to be computed grows dramatically with the size of the input random vector, which makes the computational cost of classical solution schemes (may it be intrusive (i.e.of Galerkin type) or non-intrusive) unaffordable when the deterministic finite element model is expensive to evaluate. To address such problems, this paper describes a non-intrusive method that builds a sparse PC expansion. An adaptive regression-based algorithm is proposed for automatically detecting the significant coefficients of the PC expansion. Besides the sparsity of the basis, the experimental design used at each step of the algorithm is systematically complemented in order to ensure the well-posedness of the various regression problems. The accuracy of the PC model is checked using classical tools of statistical learning theory (e.g. leave-one-out cross-validation). As a consequence, a rather small number of PC terms is eventually retained (sparse representation), which may be obtained at a reduced computational cost compared to the classical “full” PC approximation. The convergence of the algorithm is shown on an academic example. Then the method is illustrated on two stochastic finite element problems, namely a truss and a frame structure involving 10 and 21 input random variables, respectively. read more read less

Topics:

Polynomial chaos (61%)61% related to the paper, Multivariate random variable (56%)56% related to the paper, Sparse approximation (56%)56% related to the paper, Adaptive algorithm (55%)55% related to the paper, Finite element method (54%)54% related to the paper
582 Citations
Journal Article DOI: 10.1016/S0266-8920(97)00036-2
Wind field simulation

Abstract:

An efficient algorithm to simulate turbulent, atmospheric or wind tunnel generated wind fields is devised. The method is based on a model of the spectral tensor for atmospheric surface-layer turbulence at high wind speeds and can simulate two- or three-dimensional fields of one, two or three components of the wind velocity fl... An efficient algorithm to simulate turbulent, atmospheric or wind tunnel generated wind fields is devised. The method is based on a model of the spectral tensor for atmospheric surface-layer turbulence at high wind speeds and can simulate two- or three-dimensional fields of one, two or three components of the wind velocity fluctuations. The spectral tensor is compared with and adjusted to several spectral models commonly used in wind engineering. Compared to the Sandia method (see Veers, P. S., Three-dimensional wind simulation. Technical Report SAND88-0152, Sandia National Laboratories, 1988) the algorithm is more efficient, simpler to implement, and in some respects more physical. The simulation method is currently used for load calculations on wind turbines and bridges. read more read less

Topics:

Wind engineering (69%)69% related to the paper, Wind speed (66%)66% related to the paper, Wind power (61%)61% related to the paper, Wind tunnel (61%)61% related to the paper
544 Citations
open accessOpen access Journal Article DOI: 10.1016/S0266-8920(99)00028-4
A nonparametric model of random uncertainties for reduced matrix models in structural dynamics
Christian Soize1

Abstract:

Random uncertainties in finite element models in linear structural dynamics are usually modeled by using parametric models. This means that: (1) the uncertain local parameters occurring in the global mass, damping and stiffness matrices of the finite element model have to be identified; (2) appropriate probabilistic models of... Random uncertainties in finite element models in linear structural dynamics are usually modeled by using parametric models. This means that: (1) the uncertain local parameters occurring in the global mass, damping and stiffness matrices of the finite element model have to be identified; (2) appropriate probabilistic models of these uncertain parameters have to be constructed; and (3) functions mapping the domains of uncertain parameters into the global mass, damping and stiffness matrices have to be constructed. In the low-frequency range, a reduced matrix model can then be constructed using the generalized coordinates associated with the structural modes corresponding to the lowest eigenfrequencies. In this paper we propose an approach for constructing a random uncertainties model of the generalized mass, damping and stiffness matrices. This nonparametric model does not require identifying the uncertain local parameters and consequently, obviates construction of functions that map the domains of uncertain local parameters into the generalized mass, damping and stiffness matrices. This nonparametric model of random uncertainties is based on direct construction of a probabilistic model of the generalized mass, damping and stiffness matrices, which uses only the available information constituted of the mean value of the generalized mass, damping and stiffness matrices. This paper describes the explicit construction of the theory of such a nonparametric model. read more read less

Topics:

Stiffness matrix (57%)57% related to the paper, Matrix (mathematics) (56%)56% related to the paper, Parametric model (55%)55% related to the paper, Generalized coordinates (54%)54% related to the paper, Random matrix (53%)53% related to the paper
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475 Citations
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Probabilistic Engineering Mechanics format uses elsarticle-num citation style.

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Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Probabilistic Engineering Mechanics citation style.

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One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Probabilistic Engineering Mechanics's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

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SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

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To be honest, the answer is NO. The impact factor is one of the many elements that determine the quality of a journal. Few of those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo Archiving Policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

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S. No. Citation Style Type
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3. Numbered (Superscripted)
4. Author Year (Cited Pages)
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