Deadline extended to 5th of December: MS06, MS13 & MS24 at ASCE-ICVRAM-ISUMA 2024

Dear Colleague,

The deadline for the following call for abstracts has been extended to the 5th of December.

It's our pleasure to invite you to submit a two-page abstract to the three mini-symposiums entitled “MS06: Reliability Design Analysis and Optimization of Structures and Critical Infrastructures“, “MS13: AI for uncertainty quantification”, “MS24: Uncertainty Modelling and Computational Challenges in Stochastic Dynamics”, which are sessions of the 4^th International Conference on Vulnerability and Risk Analysis and Management & 8^th International Symposium on Uncertainty Modelling and Analysis (ASCE-ICVRAM-ISUMA 2024) to be held at Tongji University, Shanghai, China, from 25 to 28 April 2024. The detailed description of the three mini-symposiums can be found at:

1. MS06. Reliability Design Analysis and Optimization of Structures and Critical Infrastructures – Minisymposium – ICVRAM 2024
2. MS13. AI for Uncertainty Quantification – Minisymposium – ICVRAM 2024
3. MS24. Uncertainty Modelling and Computational Challenges in Stochastic Dynamics – Minisymposium – ICVRAM 2024

as well as below.

The deadline for abstract submission is due December 5, 2023. Please submit your abstract through the ASCE-ICVRAM-ISUMA 2024 submission system: icvram2024.org/Passport/Login. The specific details of submitting a new abstract is as follows: Step 1: If you are visiting this page for the first time, please create a new profile by clicking on the homepage “Submission” → “Create New Profile”. Once the profile has been created, it is possible to access your profile again at any time, by entering your email and password. Step 2: Please log in to fill in and save your personal information. Next please download the WORD template of the two-page abstract (“User Center” → “Submission Center” → “Submit Abstract” → “Click to download the Abstract Template”) and fill in all the information, then submit it.

We very much hope you will accept our invitation! You are very welcome to forward this email to potential contributors who wish to attend this mini-symposium!

Best wishes,

MS06 Mini-Symposium Organizers:
Yan Shi, Leibniz Universität Hannover, Hannover, Germany. Email: yan.shi@irz.uni-hannover.de
Meng-Ze Lyu, Tongji University, Shanghai, China. Email: lyumz@tongji.edu.cn
Michael Beer, Leibniz Universität Hannover, Hannover, Germany. Email: beer@irz.uni-hannover.de
Bilal M. Ayyub, University of Maryland, College Park, USA. Email: ba@umd.edu
Enrico Zio, Politecnico di Milano, Milan, Italy; Mines Paris – Université Paris Sciences & Lettres, Paris, France. E-mail: enrico.zio@polimi.it,  enrico.zio@mines-paristech.fr

MS13 Mini-Symposium Organizers:
Tong Zhou, The Hong Kong Polytechnic University, Hong Kong, China, Email: tong-cee.zhou@polyu.edu.hk
Chao Dang, Leibniz Universität Hannover, Hannover, Germany, Email: chao.dang@irz.uni-hannover.de
Yongbo Peng, Tongji University, Shanghai, China, Email: pengyongbo@tongji.edu.cn
Michael Beer, Leibniz Universität Hannover, Hannover, Germany, Email: beer@irz.uni-hannover.de
Bruno Sudret, ETH Zurich, Zurich, Switzerland, Email: sudret@ethz.ch
Enrico Zio, Politecnico di Milano, Milan, Italy. Email: enrico.zio@polimi.it

MS24 Mini-Symposium Organizers:
Marco Behrendt, Leibniz Universität Hannover, Hannover, Germany. E-mail: behrendt@irz.uni-hannover.de
Meng-Ze Lyu, Tongji University, Shanghai, China. E-mail: lyumz@tongji.edu.cn
Jian-Bing Chen, Tongji University, Shanghai, China. E-mail: chenjb@tongji.edu.cn
Michael Beer, Leibniz Universität Hannover, Hannover, Germany. E-mail: beer@irz.uni-hannover.de

MS06: Reliability Design Analysis and Optimization of Structures and Critical Infrastructures
Abstract: Engineering structures are subject to multiple uncertainties, encompassing factors such as geometric variations, material inconsistencies, and stochastic external loads. These uncertainties have the potential to substantially impact structural performance, and in more severe instances, precipitate to structural failure. The goal of reliability analysis is to quantitatively evaluate the likelihood that structures will successfully fulfill their designated functions within specified operational conditions and over defined durations. Complementary to this, the objective of reliability-based design optimization is to provide an optimal structural design that satisfies the requisite levels of reliability. The realm of reliability design analysis and optimization for structures has garnered considerable attention within domains of theorical study and engineering applications. However, the execution of reliability design analysis and optimization for intricate engineering structures remains quite an undertaking in practice, particularly in the context of high-dimensional problems and low failure probabilities. This symposium extends an invitation for contributions addressing the intricacies of reliability analysis and design optimization for engineering structures. The potential topics include models and methodologies tailored for high-dimensional structures, both in time-independent and time-dependent scenarios. Additional focus areas encompass handling multiple sources of uncertainty, advancements in reliability pertaining to engineering mechanics, utilization of surrogate models and machine learning paradigms, data-driven reliability assessment, the estimation of small failure probabilities, innovative numerical simulation techniques, and emerging tools for reliability design analysis and optimization. Contributions that tackle real-world applications and present pioneering theories within disciplines such as civil engineering, aerospace engineering, construction engineering, mechanical engineering, energy engineering, automobile engineering, and other pertinent fields are strongly encouraged and warmly welcomed.

MS13: AI for uncertainty quantification
Abstract: Uncertainty quantification (UQ) involves quantitatively characterizing all sources of uncertainties arising from both computational and real-world applications. It plays a pivotal role in various scientific and engineering domains, particularly in situations where decisions or product designs hinge on imperfectly known system aspects due to a lack of information or intrinsic randomness. A comprehensive UQ framework includes many sub-tasks such as uncertainty characterization, forward uncertainty propagation, inverse uncertainty propagation, uncertainty sensitivity analysis, etc. All the subtasks of UQ pose great challenges in numerical computation.
Artificial intelligence (AI) including machine learning is the scientific study of algorithms and statistical models that allow computers to learn from existing data without being explicitly programmed. In recent years, the application of AI in a wide range of industries has grown rapidly. Hence, it has brought new hopes for addressing UQ challenges. However, the recent developments in this area are far from mature for solving all the above-mentioned tasks. The aim of this mini-symposium is to collect the latest developments in the realm of AI for UQ, offering a platform to explore innovative approaches and solutions across all facets of uncertainty quantification.
Specific contributions related to both methodology developments and engineering applications regarding but not restricted to following aspects are welcome:

• Big data-based engineering loading modeling.
• New surrogate modeling techniques tailored to some computationally-demanding problems.
• Efficient reliability analysis methods to challenging problems.
• Efficient sensitivity analysis techniques.
• Time history predictions and time-variant reliability analysis of complex dynamic problems.
• Reliability-based design optimization using advanced algorithms.
• Physics-informed neural networks-based solutions.
• Model update with structural health monitoring.

MS24: Uncertainty Modelling and Computational Challenges in Stochastic Dynamics
Abstract: In the ever-evolving field of engineering, ensuring the reliability of structural systems is of paramount importance. Addressing the complex buildings and structures subjected to stochastic excitations, this mini-symposium highlights the importance of accounting for uncertainties, the design and modelling of input loads, and the utilization of advanced computational techniques to enhance the ability to tackle challenges in stochastic dynamics. Engineering systems are often featuring complex nonlinearities and intricate time-frequency representations. State-of-the-art modelling approaches allow for the comprehensive understanding of these complexities, resulting in significantly more precise predictions of structural stochastic behavior. Further, uncertainties are inherent in engineering problems, and their accurate consideration is vital for the dependable assessment of structural reliability. The objective of this mini-symposium is to explore innovative approaches and methodologies for characterizing, quantifying, and incorporating uncertainties into stochastic dynamics. Furthermore, the emphasis will be on approaches for uncertain load modelling and advanced computational methods, including probabilistic dependency characterization and complex spatiotemporal variability representation for various stochastic processes (fields), highlighting their pivotal role in achieving reliable simulation results. This mini-symposium will feature novel research on how computational tools and techniques can be leveraged to enhance the capacity to solve complex structural reliability problems.