Organizers
b - Institute for Risk and Reliability, University of Liverpool, Liverpool L69 7ZF, United Kingdom
c - International Joint Research Center for Resilient Infrastructure & International Joint Research Center for Engineering Reliability and Stochastic Mechanics, Tongji University, Shanghai 200092, China
d - MINES Paris-PSL Research University, CRC, Sophia Antipolis 06560, France
e - Energy Department, Politecnico di Milano, Milano 20156, Italy
f - Civil and Environmental Engineering, University of Strathclyde, Glasgow G1 1XJ, United Kingdom
g - School of Mechanical and Electrical Engineering, University Electronic Science and Technology of China, Chengdu 611731, China
h - Center for System Reliability and Safety, University Electronic Science and Technology of China, Chengdu 611731, China
Abstract
Engineering structures, systems and components (SSCs) always exist multiple uncertainties such as vibration and deformation uncertainties, which may affect the function of SSCs or lead to the failure of SSCs. Reliability analysis aims to assess the probability that SSCs complete their specific tasks under specific conditions of operation and for a specified time. However, reliability analysis of complex engineering SSCs is still challenging for high-dimensional problems and small failure probability estimation of highly reliable SSCs. This mini-symposium invites contributions dealing with the reliability analysis of SSCs in complex situations. A non-exhaustive list of topics involves reliability models and methods for high-dimensional SSCs, small failure probability estimation, multiple uncertainties, progress in reliability related to engineering mechanics, surrogate models and machine learning approaches, reliability assessment based on data, reliability-assisted optimization, advanced numerical simulation techniques, and novel reliability analysis tools. Contributions dealing with challenging applications and innovation of theories in energy, oil, gas, aerospace, construction, machinery, automobile, shipbuilding and many other engineering mechanics-related fields are quite welcome.
Email
beer@irz.uni-hannover.de (Michael Beer)
enrico.zio@mines-paristech.fr; enrico.zio@polimi.it (Enrico Zio)
edoardo.patelli@strath.ac.uk (Edoardo Patelli)
yan.shi@irz.uni-hannover.de (Yan Shi)
- Michael Beer a, b, c
- Enrico Zio d, e
- Edoardo Patelli f
- Yan Shi a, g, h
b - Institute for Risk and Reliability, University of Liverpool, Liverpool L69 7ZF, United Kingdom
c - International Joint Research Center for Resilient Infrastructure & International Joint Research Center for Engineering Reliability and Stochastic Mechanics, Tongji University, Shanghai 200092, China
d - MINES Paris-PSL Research University, CRC, Sophia Antipolis 06560, France
e - Energy Department, Politecnico di Milano, Milano 20156, Italy
f - Civil and Environmental Engineering, University of Strathclyde, Glasgow G1 1XJ, United Kingdom
g - School of Mechanical and Electrical Engineering, University Electronic Science and Technology of China, Chengdu 611731, China
h - Center for System Reliability and Safety, University Electronic Science and Technology of China, Chengdu 611731, China
Abstract
Engineering structures, systems and components (SSCs) always exist multiple uncertainties such as vibration and deformation uncertainties, which may affect the function of SSCs or lead to the failure of SSCs. Reliability analysis aims to assess the probability that SSCs complete their specific tasks under specific conditions of operation and for a specified time. However, reliability analysis of complex engineering SSCs is still challenging for high-dimensional problems and small failure probability estimation of highly reliable SSCs. This mini-symposium invites contributions dealing with the reliability analysis of SSCs in complex situations. A non-exhaustive list of topics involves reliability models and methods for high-dimensional SSCs, small failure probability estimation, multiple uncertainties, progress in reliability related to engineering mechanics, surrogate models and machine learning approaches, reliability assessment based on data, reliability-assisted optimization, advanced numerical simulation techniques, and novel reliability analysis tools. Contributions dealing with challenging applications and innovation of theories in energy, oil, gas, aerospace, construction, machinery, automobile, shipbuilding and many other engineering mechanics-related fields are quite welcome.
beer@irz.uni-hannover.de (Michael Beer)
enrico.zio@mines-paristech.fr; enrico.zio@polimi.it (Enrico Zio)
edoardo.patelli@strath.ac.uk (Edoardo Patelli)
yan.shi@irz.uni-hannover.de (Yan Shi)
