[SIPTA News] FW: Call for abstracts for ISRERM 2024

FYI, imprecise probabilities are one of the highlighted topics

From: beer@irz.uni-hannover.de<mailto:beer@irz.uni-hannover.de> <beer@irz.uni-hannover.de<mailto:beer@irz.uni-hannover.de>> Sent: Monday, January 29, 2024 9:35 AM [Institute for Risk and Reliability Logo] [University of Hannover Logo]

Call for abstracts for ISRERM 2024

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Dear Colleague,

It's our pleasure to invite you to submit an abstract to the International Symposium on Reliability Engineering and Risk Management (ISRERM) to be held at Hefei University of Technology, China, from October 18th to 21st, 2024. Please also take a look at several mini-symposia. The detailed description of the these mini-symposiums can be found here<http://cossan.irz.uni-hannover.de/lists/lt.php?tid=KkpfVQRSBlcGWR8FDANXSQwOUwYcBVMJUE4HVQABUFEDVwQEAlFIAQEPVgRcBwBJC1UJCBxQVQwETg4HBFIbW1UFAlUCXQRUAVoKTV9fCQZeBAQDHFMODQROA1cFBxsDWgFfHAUHAVEBC1MHCFVQUw> as well as below.

The International Symposium on Reliability Engineering and Risk Management (ISRERM) is a significant biennial international conference that serves as a pivotal platform for the exchange of knowledge and innovative applications in the fields of reliability-based, risk-based, and uncertainty-informed decision making. Its primary focus lies in addressing safety-related aspects throughout the entire lifecycle of engineering systems.

Over the years, ISRERM has garnered international recognition, with a rich history of hosting conferences at esteemed institutions worldwide. Past symposiums have been hosted at prestigious locations such as Tongji University, Shanghai, China (2008, 2010), Kanagawa University, Yokohama, Japan (2012), Taiwan University of Science & Technology, Chinese Taipei (2014), Yonsei University, Seoul, South Korea (2016), National University of Singapore, Singapore (2018), Beijing University of Technology, China (2020), and Leibniz University Hannover, Germany (2022). It is with great excitement that we announce the forthcoming 9th ISRERM, scheduled to be held at Hefei University of Technology, China, from October 18th to 21st, 2024.

ISRERM serves as a platform fostering interdisciplinary dialogue on reliability assessment, risk and uncertainty quantification, mitigation, and management, as well as effective decision-making strategies. Our primary objective is to bridge the knowledge gaps among diverse disciplines grappling with similar challenges and to transform scholarly discussions into strong frameworks for dealing with emerging reliability and risk management problems.

The deadline for the abstract submission is February 29, 2024. The official registered submission website is as follows: https://isrerm2024.aconf.org<https://isrerm2024.aconf.org/>

1. If you are visiting this page for the first time, please create a new profile by clicking on the homepage "Sign up". Once the profile has been created, it is possible to access your profile again at any time, by entering your email and password.
2. Please log in to fill in and save your personal information ("Dashboard" → “My Profile"). Next click the “Downloads” tab on the left side of homepage to download the template file of the abstract (ISRERM_2024_Abstract_templates.zip).
3. Submit the abstract in the submission area. You can get to the submission area through the “Submission” tab on the left side of homepage.

Information about Registration, Exhibition, and Sponsorship opportunities will be made available soon. Please visit regularly the symposium website for news and updates.

If you have any question, please feel free to contact the organizers via the following e-mail address: isrerm2024@hfut.edu.cn<mailto:isrerm2024@hfut.edu.cn>

With warm regards

Yours sincerely

Jie Li, Conference Chair, Academician of CAS, EASA, University Distinguished Chair Professor, Tongji University

Jingfeng Wang, Chair of Local Organizing Committee, Professor, Hefei University of Technology

Lunhai Zhi, Co-Chair of Local Organizing Committee, Professor, Hefei University of Technology

Yongbo Peng, Co-Chair of Local Organizing Committee, Professor, Tongji University

ISRERM 2024 Secretariat

Hefei University of Technology

Hefei 230009, China

Email: isrerm2024@hfut.edu.cn<mailto:isrerm2024@hfut.edu.cn>

Website: https://isrerm2024.aconf.org<https://isrerm2024.aconf.org/>

MS03: Uncertainty Modeling and Quantification for Model Updating and Structural Health Monitoring Abstract: Structural Health Monitoring (SHM) aims at condition assessment and service life monitoring of structural systems, often based on the availability of system vibration data. Model updating has been developed as a key technique for SHM, where the parameters or the numerical model itself are calibrated to tune the model prediction close to the actual system behaviour. However, uncertainties are inevitable in both the measuring and modeling processes, which leads to the necessity of non-deterministic approaches to modeling and quantifying the uncertainties. This Mini-Symposium is dedicated to gathering experts from both academia and industries to showcase the latest advances on the uncertainty treatment in model updating and SHM and their practical applications.

• Topics for potential contributions include but are not limited to:
• Stochastic/interval model updating;
• Inverse quantification of mixed aleatory and epistemic uncertainty;
• On-line model updating for nonlinear dynamic systems;
• Model updating and SHM applications in large-scale structures.

MS03 Organizers:

• Masaru Kitahara, The University of Tokyo, Tokyo, Japan. E-mail: kitahara@bridge.t.u-tokyo.ac.jp<mailto:kitahara@bridge.t.u-tokyo.ac.jp>
• Michael Beer, Leibniz University Hannover, Hannover, Germany. E-mail: beer@irz.uni-hannover.de<mailto:beer@irz.uni-hannover.de>
• Siu-Kui Au, Nanyang Technological University, Singapore, Singapore. E-mail: ivanau@ntu.edu.sg<mailto:ivanau@ntu.edu.sg>
• Takeshi Kitahara, Kanto Gakuin University, Yokohama, Japan. E-mail: kitahara@kanto-gakuin.ac.jp<mailto:kitahara@kanto-gakuin.ac.jp>

MS05: Stochastic finite element methods and their applications on model updating Abstract: Stochastic finite element method is an important tool widely used in many aspects such as reliability analysis, safety assessment and model updating et al. At present, various stochastic finite element methods have been developed. As typical representatives among these methods, the perturbation methods and the Garlekin based methods like PC and GPC, have been successfully applied to solve the stochastic problems in extensive industrial fields. However, highly accurate and efficient stochastic finite element methods are still developing to implement the stochastic analysis of large-scale structural models. This research direction has been attracting many researchers’ attention.

Model updating is a key content in structural health monitoring. When considering the randomness of structural modelling and measurement data, the stochastic model updating becomes unavoidable. To solve stochastic model updating problems, the stochastic finite element methods can play a significant role instead of the usually used Bayesian methods. This direction is a hot topic in current structural model updating, which has a very good application prospect in practical structural health monitoring.

The symposium aspires to create a conducive environment for scholars, researchers, and practitioners to exchange insights and findings, fostering collaborative endeavors and innovations in the domain of stochastic finite element method. The inclusion of diverse theoretical explorations and practical applications is anticipated to provide a comprehensive perspective on the evolving role of stochastic finite element method to address the inverse problem in health monitoring.

Topics for potential contributions include but are not limited to:

• Recent advances in stochastic finite element method.
• Advanced computational techniques in Stochastic Static and Dynamics.
• Recent mathematical and numerical developments in stochastic structural engineering static and dynamics.
• New surrogate modeling techniques tailored to some computationally-demanding problems.
• Stochastic finite element method in reliability analysis, safety assessment.
• Stochastic model updating.
• Model update with structural health monitoring.
• Engineering practice of structural health monitoring accommodating uncertainties.
• Novel uncertainty quantification techniques in model updating.
• Data-driven approaches for model updating.

MS05 Organizers:

• Prof. Dr. Bin Huang, Wuhan university of technology, Wuhan, China. E-mail: binhuang@whut.edu.cn<mailto:binhuang@whut.edu.cn>
• Dr. Heng Zhang, Yangtze University, Jinzhou, China. E-mail: supzhangheng@163.com<mailto:supzhangheng@163.com>
• Dr. Hui Chen, College of Post and Telecommunication, Wuhan Institute of Technology, Wuhan, China. E-mail: huichenvip@163.com<mailto:huichenvip@163.com>
• Dr. Zhifeng Wu, Huazhong University of Science and Technology, Wuhan, China. E-mail: wuzhifeng@hust.edu.cn<mailto:wuzhifeng@hust.edu.cn>
• Prof. Dr. Michael Beer, head of Institute for Risk and Reliability, Leibniz Universität Hannover (LUH), Germany. E-mail: beer@irz.uni-hannover.de<mailto:beer@irz.uni-hannover.de>
• Prof. Sifeng Bi, University of Strathclyde, Glasgow, United Kingdom. E-mail: sifeng.bi@strath.ac.uk<mailto:sifeng.bi@strath.ac.uk>

MS08: Data-driven and Uncertainty-aware Modeling of Complex and Nonlinear Systems Abstract: The recent years have seen significant progress in modeling complex and nonlinear systems, with a notable shift towards data-driven methodologies and a heightened recognition of the inherent uncertainties associated with such systems. Non-deterministic modeling approaches, including probabilistic, interval, fuzzy, or imprecise methods, necessitate accurate specification of uncertain model parameters. However, direct measurement of these quantities is often impractical or cost-prohibitive, leading to the widespread application of data-driven and uncertainty-aware modeling techniques.

This mini-symposium aims to bring together experts, researchers, and practitioners from academia and industry to explore and discuss the latest developments, methodologies, and applications related to data-driven modeling and the incorporation of uncertainty-aware techniques in understanding complex and nonlinear systems. Papers discussing advances in techniques from both uncertainty forward and inverse propagation by probabilistic theory, as well as interval methods and concepts based on imprecise probabilities are invited. Next to these non-deterministic and data-driven approaches for numerical modelling, stochastic model updating, system identification, damage localization, sensor placement optimization is highly welcomed.

Topics for potential contributions include but are not limited to:

• Stochastic model updating
• Highly efficient uncertainty propagation
• Quantification of multiple uncertainty sources
• Data-driven modelling
• Imprecise probability
• System identification
• Sensor placement optimization
• Digital twin

MS08 Organizers:

• Yanlin Zhao, University of Science and Technology Beijing, Beijing, China. E-mail: zhaoyanlin@ustb.edu.cn<mailto:zhaoyanlin@ustb.edu.cn>
• Yongtao Bai, Chongqing University, Chongqing, China. E-mail: bai.yongtao@cqu.edu.cn<mailto:bai.yongtao@cqu.edu.cn>
• Sifeng Bi, University of Strathclyde, Glasgow, United Kingdom. E-mail: Sifeng.bi@strath.ac.uk<mailto:Sifeng.bi@strath.ac.uk>
• Michael Beer, Leibniz University Hannover, Hannover, Germany. E-mail: beer@irz.uni-hannover.de<mailto:beer@irz.uni-hannover.de>
• David Moens, Katholieke Universiteit Leuven, Leuven, Belgium. E-mail: david.moens@kuleuven.be<mailto:david.moens@kuleuven.be>

MS09: Addressing Aleatoric and Epistemic Uncertainty with Imprecise Probabilities Abstract: Uncertainties appearing in engineering problems can be broadly classified as aleatoric and epistemic. The first group refers to intrinsic uncertainty stemming out of randomness, while the latter reflects lack of knowledge, conflicting sources of information, etc. One possibility for tackling such class of problems is resorting to imprecise probabilities which, in essence, can be interpreted as a collection of classical probabilistic models that account for aleatoric uncertainty and which are indexed by, e.g. interval variables that capture epistemic uncertainty. Although imprecise probabilities offer an extremely wide and flexible framework, its practical deployment is not devoid of challenges. Therefore, the aim of this MS is bringing together some of the latest developments on imprecise probabilities, including (but not limited to):

• Different models for representing uncertainty such as, e.g. p-boxes, imprecise stochastic processes, distribution-free models, etc.
• Novel formulations for coping with aleatoric and epistemic uncertainty, such as advanced simulation methods; surrogate models, etc.
• Inverse uncertainty quantification in the presence of aleatoric and epistemic uncertainty.
• Practical applications involving challenging engineering problems.

MS09 Organizers:

• Marcos Valdebenito, TU Dortmund University, Germany, marcos.valdebenito@tu-dortmund.de<mailto:marcos.valdebenito@tu-dortmund.de>
• Michael Beer, Leibniz University Hannover, Germany. E-mail: beer@irz.uni-hannover.de<mailto:beer@irz.uni-hannover.de>
• Matthias Faes, TU Dortmund University, Germany. E-mail: matthias.faes@tu-dortmund.de<mailto:matthias.faes@tu-dortmund.de>
• Zhan Kang, Dalian University of Technology, China. E-mail: zhankang@dlut.edu.cn<mailto:zhankang@dlut.edu.cn>
• Chao Jiang, Hunan University, China. E-mail: jiangc@hnu.edu.cn<mailto:jiangc@hnu.edu.cn>

MS14: Global Reliability Analysis of Complex Nonlinear Stochastic Dynamical Systems Abstract: The study of complex nonlinear stochastic dynamical systems is of paramount importance in both science and engineering. These systems exhibit a myriad of uncertainties, stemming from various sources such as material properties, external excitations, and inherent nonlinearities. These uncertainties introduce significant ambiguity into the response of systems, making precise analysis and design a formidable challenge. The coupling of nonlinearity and stochasticity necessitates a refined understanding of uncertainty propagation, which is indispensable for ensuring the safety and reliability of engineering systems. This symposium invites you to contribute to the discussion on the complexities of global reliability analysis in engineering structures. We welcome contributions that address, but are not limited to, the following themes:

• Novel approaches for modeling and analyzing complex nonlinear systems subjected to stochastic influences.
• Techniques for addressing the safety challenges associated with high-dimensional uncertainty inputs in engineering systems.
• Methods to assess the propagation of stochastic effects in high-degree-of-freedom nonlinear systems.
• Exploration of reliability analysis in emerging dynamic systems, such as those involving fractional-order differentiation or negative stiffness mechanisms.
• Development of efficient and accurate methods for advanced global reliability assessment in complex systems.
• Leveraging surrogate models and machine learning approaches to enhance reliability analysis in nonlinear stochastic dynamical systems.
• Innovative strategies that combine physical understanding with data-driven approaches for more robust reliability analysis.
• Techniques for precisely assessing low failure probabilities under rare events, critical in high-consequence systems.
• Effective and efficient reliability methods in the context of reliability-based design optimization of complex nonlinear stochastic dynamical systems.
• optimization of complex nonlinear stochastic dynamical systems.
• We strongly encourage and warmly welcome contributions that address real-world applications and propose pioneering theories within disciplines such as civil engineering, aerospace engineering, construction engineering, mechanical engineering, energy engineering, automotive engineering, and other relevant fields.

MS14 Organizers:

• Meng-Ze Lyu, Tongji University, Shanghai, China. E-mail: lyumz@tongji.edu.cn<mailto:lyumz@tongji.edu.cn>
• Jian-Bing Chen, Tongji University, Shanghai, China. E-mail: chenjb@tongji.edu.cn<mailto:chenjb@tongji.edu.cn>
• Michael Beer, Leibniz Universität Hannover, Hannover, Germany. E-mail: beer@irz.uni-hannover.de<mailto:beer@irz.uni-hannover.de>
• Hector Jensen, Universidad Técnica Federico Santa María, Valparaíso, Chile. E-mail: hector.jensen@usm.cl<mailto:hector.jensen@usm.cl>

MS24: Quantification and Propagation of Uncertainties in Stochastic Dynamics: Computational Challenges and Solutions Abstract: This mini-symposium is dedicated to exploring the computational challenges of modeling uncertainties in dynamical systems and loads, with a particular focus on improving the assessment of structural reliability. The symposium will address various dimensions of uncertainty, including stochastic excitations, structural reliability, and the sophisticated task of accounting for uncertainties in input load modeling. The aim is to highlight the use of advanced computational techniques that play a central role in improving the accuracy and reliability of dynamical system models. It provides a platform for the exchange of insights and strategies between researchers and facilitates a comprehensive understanding of the complexity involved in dealing with uncertainties.

The topics to be discussed include, but are not limited to:

This mini-symposium invites researchers to contribute their expertise and insights to enhance collaborative discussions that improve the understanding and application of computational methods in the treatment of uncertainties in stochastic dynamics.

• Reliability of structural systems: Exploring methods and concepts for assessing and improving the reliability of structural systems under dynamic conditions.
• Stochastic excitations: Exploring the effects of stochastic excitations on dynamical systems and discussing methods to seamlessly incorporate uncertainties into modeling frameworks.
• Accounting for uncertainties: Exploring strategies for robust consideration of uncertainties, with a focus on practical applications and implications for structural analysis.
• Advanced computational techniques: Emphasizing the role of advanced computational techniques in mitigating the computational challenges posed by dynamical systems with uncertainties.

This mini-symposium invites researchers to contribute their expertise and insights to enhance collaborative discussions that improve the understanding and application of computational methods in the treatment of uncertainties in stochastic dynamics.

MS24 Organizers:

• Marco Behrendt, Leibniz Universität Hannover, Hannover, Germany. E-mail: behrendt@irz.uni-hannover.de<mailto:behrendt@irz.uni-hannover.de>
• Meng-Ze Lyu, Tongji University, Shanghai, China. E-mail: lyumz@tongji.edu.cn<mailto:lyumz@tongji.edu.cn>
• Jian-Bing Chen, Tongji University, Shanghai, China. E-mail: chenjb@tongji.edu.cn<mailto:chenjb@tongji.edu.cn>
• Michael Beer, Leibniz Universität Hannover, Hannover, Germany. E-mail: beer@irz.uni-hannover.de<mailto:beer@irz.uni-hannover.de>

MS31: Uncertainty quantification in wind engineering Abstract: The field of wind engineering plays a pivotal role in ensuring the safety and resilience of structures against the dynamic wind forces. However, the inherent uncertainty associated with both the extreme wind loads and structural dynamic behaviors pose a significant challenge to accurate and reliable structural design. Illustrative instances of these uncertainties include the stochastic nature of wind excitations, challenges in accurately estimating the wind power spectrum, and uncertainties in structural aerodynamics. By bringing together researchers, practitioners, and industry professionals, this mini-symposium aims to foster collaboration and innovation in the pursuit of more robust and resilient wind engineering practices.

Topics for potential contributions include but are not limited to:

• Probabilistic Models: Presentations will delve into the development and application of probabilistic models to characterize the uncertainty in wind loads. Discussions will focus on incorporating meteorological data, turbulence models, and environmental factors into probabilistic frameworks;
• Data-Driven Approaches: This session will explore innovative data-driven methods for uncertainty quantification, leveraging machine learning and big data analytics to enhance the accuracy of wind load predictions. Case studies highlighting the practical implementation of these approaches will be emphasized;
• Sensitivity Analysis: Understanding the sensitivity of structures to uncertain parameters is crucial. This segment will cover sensitivity analysis techniques, assessing the impact of varying wind conditions, structural configurations, and terrain features on the overall uncertainty;
• Industry Perspectives: Experts from both academia and industry will share insights into the practical implications of uncertainty quantification in wind engineering. Presentations will address real-world challenges faced by engineers and showcase successful applications of uncertainty-aware design.

MS31 Organizers:

• Xu Hong, Hefei University of Technology, Hefei, China. E-mail: xhong@hfut.edu.cn<mailto:xhong@hfut.edu.cn>
• Genshen Fang, Tongji University, Shanghai, China. E-mail: 2222tjfgs@tongji.edu.cn<mailto:2222tjfgs@tongji.edu.cn>
• Zhiqiang Wan, Northwestern Polytechnical University, China, E-mail: wanzhiqiang@nwpu.edu.cn<mailto:wanzhiqiang@nwpu.edu.cn>
• Lunhai Zhi, Hefei University of Technology, Hefei, China. E-mail: zhilunhai1979@163.com<mailto:zhilunhai1979@163.com>
• Michael Beer, Leibniz University Hannover, Germany, E-mail: beer@irz.uni-hannover.de<mailto:beer@irz.uni-hannover.de>
• Seymour M.J. Spence, University of Michigan, US, E-mail: smjs@umich.edu<mailto:smjs@umich.edu>

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