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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering is a peer-reviewed journal published by ASME, covering Probabilistic and Robust Engineering Design, Risk and Safety Analysis, Structural Health Monitoring Techniques. Over its lifetime it has published 516 papers, which have received 4,084 citations.
| Publisher | ASME |
|---|---|
| Reference style | Numbered (ASME) Numbered — [1], [2] in the text [1] Smith, A., Jones, B., and Lee, C., 2023, "A Representative Article Title," ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 12(3), pp. 45–58.
Formats any DOI in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering style. No sign-up. |
| Publishes research in | Probabilistic and Robust Engineering Design Risk and Safety Analysis Structural Health Monitoring Techniques Advanced Multi-Objective Optimization Algorithms Reliability and Maintenance Optimization |
| ISSN | 2332-9017 |
| Citation impact (2-yr) | 1.43 |
| h-index | 27 |
| i10-index | 128 |
| Top institutions publishing here | University of Maryland, College Park |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract This paper presents a review of the state of the art for digital twins in the application domain of engineering dynamics. The focus on applications in dynamics is because: (i) they offer some of the most challenging aspects of creating an effective digital twin, and (ii) they are relevant to important industrial applications such…
Abstract Predictive maintenance, as a form of pro-active maintenance, has increasing usage and shows significant superiority over the corrective and preventive maintenance. However, conventional methods of predictive maintenance have noteworthy limitations in maintenance optimization and reliability improvement. In the last two decades, machine learning has flourished and overcome many inherent flaws of conventional maintenance prediction…
Abstract The transitional Markov chain Monte Carlo (TMCMC) is one of the efficient algorithms for performing Markov chain Monte Carlo (MCMC) in the context of Bayesian uncertainty quantification in parallel computing architectures. However, the features that are associated with its efficient sampling are also responsible for its introducing of bias in the sampling. We demonstrate…
Abstract To investigate the resilience of interconnected critical infrastructures (CIs), a framework combining dynamic modeling and resilience analysis is proposed. Resilience is defined in this work as the capacity of a system to absorb the impacts of perturbations and recover quickly from disruptive states. It is seen as a property of the system, which depends…
Abstract As autonomous vehicle (AV) intelligence for controllability continues to develop, involving increasingly complex and interconnected systems, the maturity level of AV technology increasingly depends on the systems reliability level, also considering the interactions among them. Hazard analysis is typically used to identify potential system risks and avoid loss of AV system functionality. Conventional hazard…
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