ASCE-ASME Journal of Risk and Uncertainty in Engineering

Pleased to share that our technical paper which delves into the development of a mathematical framework to perform reliability analysis on complex coherent systems, under limited data, with Confidence-boxes is finally published in the ASCE-ASME Journal of Risk and Uncertainty in Engineering Part A: Civil Engineering. The research work is the result of an international collaboration between the University of Liverpool, Université de Technologie de Compiègne, Thailand Institute of Nuclear Technology, and the National University of Singapore, which spans across four countries: 🇬🇧 🇨🇵 🇹🇭 🇸🇬 We would like to extend our gratitude to Professors Sebastien Destercke, Mohamed Sallak, Scott Ferson, Dr Sicong Xiao, and Wasin Vechgama for their technical support and guidance during the course of realizing the work. Without them, we would not have made it, and for that, Thank YOU!!! 🙏 The published work aims to fulfill three objectives: 1) 🔎 to review the mathematical formalism behind the "AND" and "OR" logical operations under independence and uncertain dependencies between components for reliability analysis; 2) 🔎to review the mathematical framework behind the non-consonant confidence-boxes, and its applicability towards computing "tail-risks" (i.e., one-sided risk probability); 3) 📖 to illustrate and evaluate the strengths and limitations of the proposed framework to compute reliability with confidence-boxes via three case studies: • Pressure vessel system • Nuclear reactor system • Bridge system; 4)💡to provide perspectives on the research gaps and the future works that can be undertaken from here on. As such, the scientific contributions of the paper are: • 📕 to allow for the propagation of uncertainties through a Boolean mathematical structure given only the "k-out-of-N" reliability data; • 🔎 to provide a frequentist approach to computing the reliability of complex coherent systems with confidence intervals, whilst providing a form of statistical guarantee on the results; • 📝 to extend the reliability analysis with imprecise probabilities by considering the uncertain dependencies between the system components; • 💡 to provide insights on the research gaps that are yet to be addressed by the work, thereby highlighting the future research directions on the topic. We hope that the work would serve its intended purpose, and provides a significant contribution to the risk and reliability engineering field. Your support is greatly appreciated. Feedbacks and suggestions to move this work forward are more than welcomed! Access to the paper: https://lnkd.in/eArAcnKv #ProbabilisticRiskAssessment #ReliabilityAnalysis #ImpreciseProbability #ProbailityBoundsAnalysis #CivilEngineering #MonteCarlo #Safety #Year2 #PostDocLife #SNRSI #TrustTheProcess #AllOrNothing #ASCE

In this paper, a mode sensitivity enhancement method for beam bridge using high-density strain feedback is proposed, in which high density dynamic strain measurements of the girder structure can be collected by distributed fiber sensor. https://lnkd.in/gqTbMkBw

This paper demonstrates that even for small levels of equivalent viscous damping, a combination of sources can have a significant impact on the estimate of the fragility of a wind turbine in operational conditions. https://lnkd.in/gJvtS9jF

This paper focuses on the adjustment of multipliers through Bayesian inference based on Monte Carlo techniques using the experimental results from simulators. https://lnkd.in/g_wKvp3h

In this paper, torsional-flutter-based apparatus is investigated to extract wind energy. A nonlinear hybrid restoring toque mechanism, installed at equally spaced supports, is used to produce energy through limit-cycle vibration. https://lnkd.in/g2j64R2X

This work explores the application of control variates (CV) to a crashworthiness analysis scenario, resulting in control variates being a powerful technique to extract valuable information from limited data sets. https://lnkd.in/g8dceuqu

This paper established a digital twin to investigate the optimal design for silicon anode under the uncertainties of additive manufacturing and battery usage. https://lnkd.in/gzXtFvzv

This paper evaluates the applicability of different surrogate models engaged in computing global design variable sensitivities for the drawability assessment of a deep-drawn component. https://lnkd.in/gj26KBe4

This paper develops a new uncertainty quantification (UQ) methodology based on an existing concept of combining convolutional neural network (CNN) and Gaussian process (GP) regression (GPR). https://lnkd.in/g7rbTruk

In this research, a data-driven physics-based methodology is proposed to predict the mechanical properties of fused filament fabrication (FFF) parts using Bayesian inference. https://lnkd.in/g5VPB_Rd