The aim of a special session/panel is to bring together researchers working on a specific topic of interests.

If you are interested in organizing a special session/panel, please send your proposal to the conference secretariat at icqrms@uestc.edu.cn as early as possible. The proposal should include the session/panel title, the scope and motivation of the session/panel, and the organizers and their brief CVs.

Both full papers and extended abstracts can be submitted to the conference and the special session should include more than 5 submissions.

■  Important Dates

• Full Paper Submission Deadline        April 30, 2026
• Full Paper Acceptance Notification   May 20, 2026
• Camera Ready Papers Due                    June 5, 2026

■  Submission Notices

If submitting to a special session/panel, please send a copy of the submission to the special session/panel chair(s), indicating paper ID.

Special Session #1. Reliability and Safety of Coastal and Offshore Infrastructures, by Zifei Xu (zifei.xu@liverpool.ac.uk), University of Liverpool, UK；Huanhuan Li(huanhuan.li@soton.ac.uk), University of Southampton, UK ; Zhongchi Liu(zhongchi.liu@centec.tecnico.ulisboa.pt), University of Lisbon, Portugal ; Xiangyu Kong(xiangyu.kong@centec.tecnico.ulisboa.pt), University of Lisbon, Portugal.

Special Session #2. Advanced Reliability and Safety Assessment of Marine and Offshore Structures, by Yunling Ye (yeyunling@whut.edu.cn), Wuhan University of Technology, China；Mengzhen Li(mengzhen.li@whut.edu.cn), Wuhan University of Technology, China; Yiheng Zhang(zhangyiheng@whut.edu.cn), Wuhan University of Technology, China; Ying Tang(2320236082@nue.edu.cn), Naval University of Engineering, China.

Special Session #3. AI-Enhanced Safety and Reliability Assurance for Nuclear Installations, by Jun Yang (youngjun51@hotmmail.com), South China University of Technology, China；Guohua Wu(wugh09@163.com), Shenzhen Technology University, China; Bing Zhang(xiaohan1123@163.com), State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipmen, China; Zhihui Xu(xu_zhihui_cool@126.com), State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China; Ming Yang(mingyang@szu.edu.cn), Shenzhen University, China; Hidekazu Yoshikawa(yoshikawa.hidekazu.37c@st.kyoto-u.ac.jp), Kyoto University, Japan; Takeshi Matsuoka(mats@cc.utsunomiya-u.ac.jp),Utsunomiya University, Japan.

Special Session #4.  Life-Cycle Resilience of Civil Infrastructures under Climate Change, by You Dong ( you.dong@polyu.edu.hk), The Hong Kong Polytechnic University, Hong Kong, China；Hongyuan Guo(hong-yuan.guo@connect.polyu.hk), The Hong Kong Polytechnic University, Hong Kong, China.

Special Session #5. Reliability-centric Lifecycle Management for Marine and Offshore Assets, by Mingxin Li (m.li@bridge.t.u-tokyo.ac.jp), The University of Tokyo, Japan；Depeng Liu(liudepeng@cup.edu.cn), China University of Petroleum, China; Haoran Ye(yehr@dlmu.edu.cn), Dalian Maritime University, China; Chenfeng Huang(chenfengh@dlmu.edu.cn), Dalian Maritime University, China; Yu Sun(sunyu9@hrbeu.edu.cn), Harbin Engineering University, China.

Special Session #6. Data-driven reliability analysis for in-service infrastructures, by Yi Zhang (zhang_yi@seu.edu.cnp), Southeast University, China； Zhaohui Lu(luzhaohui@bjut.edu.cn), Beijing University of Technology, China; Peipei Li(li.peipei@bjut.edu.cn), Beijing University of Technology, China; Yide Zheng(zhengyd25@szu.edu.cn), Shenzhen University, China.

■ Keynote Speaker 1

Zhen He, PhD, Chair Professor,
Dean of the College of Management and Economics
Tianjin University, Tianjin, China

Title: Quality Management Innovation Empowered by AI
Time: July 22-25, 2026

Abstract: With the advancement of intelligent technologies such as large language models, the Internet of Things (IoT), and sensors, artificial intelligence (AI) has entered a new stage of rapid development. AI is reshaping the paradigm of quality management, bringing both opportunities and challenges to quality science research. This report proposes a pathway for the intelligent transformation of quality management based on the application logic of digitalization, networking, platform , informatization, knowledge, and intelligence. It analyzes how AI technologies empower quality management across design quality, process quality, supply chain quality, and service quality, and presents commercial application cases. The report discusses several critical issues in the application of AI to quality management, including data quality, data integration, model trustworthiness, leadership, and change management, and identifies new research topics urgently needed for quality management in the context of intelligent manufacturing. Finally, it analyzes the relationship between quality and innovation, proposing that in the intelligent manufacturing context, the integration and convergence of quality and innovation warrant in-depth investigation at both theoretical research and industrial application levels.

Biography: 

Dr. Zhen HE is a Chair Professor and Dean of the College of Management and Economics, Tianjin University. He is a Chang Jiang Scholarship Distinguished Professor of the Ministry of Education and recipient of the NSFC Distinguished Young Scientist Project. He is also an Academician of the International Academy for Quality (IAQ Academician), Fellow and Council Member of the Asia Pacific Industrial Engineering and Management Society (APIEMS). Zhen He is mainly engaged in teaching and research in the fields of quality management and quality engineering with more than 200 papers published in SCI/SSCI journals. He has led 3 key projects and 4 general projects of the National Natural Science Foundation of China, as well as 2 international cooperation projects. His research achievements have won 3 first prizes and 5 second prizes at the provincial and ministerial level.  He serves as an Area Editor of the international journal Computers and Industrial Engineering, and Editorial Board Member of several journals including International Journal of Lean Six Sigma. He has provided quality management consulting, training, or project cooperation for more than 50 enterprises including Huawei, Midea, Haier, Baosteel, TISCO.

■ Keynote Speaker 2

Prof. Suk Joo Bae, PhD, Professor,
Director in Intelligent Bigdata Center
Department of Industrial Engineering, Hanyang University, Korea

Title: Data-Driven Reliability Prediction for Fuel Cell Systems

Time: July 22-25, 2026

Abstract: Fuel cells (FCs) have received much attention as potential alternatives to current battery technologies for electronic vehicles and energy storage systems in terms of their safe applications. The state-of-art FCs have had a difficulty in commercialization in terms of reliability and cost. To improve reliability of FCs, I will present degradation models for FCs under various environment conditions and reliability prediction methods through accelerated degradation testing.  Reliability evaluation techniques are mainly based on statistical and AI-based degradation modeling of the FCs.  New reliability issues for fuel cells are also discussed in keynote speech.

Biography:

Prof. Suk Joo Bae is a Professor in Hanyang University, Seoul, Republic of Korea. He was a Provost in Graduate School at Hanyang University, 2021-2023. Prof. Bae received his PhD. from the ISyE Department at Georgia Tech, 2003. He was the Editor-in-Chief of Journal of the Korean Society for Quality Management, The Journal of Applied Reliability, and the Associate Editor of IEEE Transactions on Reliability, Informs Journal on Data Science. He is currently the Associate Editor in IISE Transactions DSQR Department, editorial board in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering from 2022. He is currently the President of Korea Reliability Society and he was a President, Korean Society for Prognostics and Health Management (PHM), 2023.  Prof. Bae has published more than 100 journal papers including Technometrics, Journal of Quality Technology, Reliability Engineering & System Safety, IISE Transactions, and IEEE Transactions on Reliability, Mechanical Systems and Signal Processing.

■ Keynote Speaker 3

Zhisheng Ye, PhD, Dean’s Chair Professor,
Department of Industrial Systems Engineering & Management
National University of Singapore

Title: Optimal Abort Policy for Mission-Critical Systems under Imperfect Condition Monitoring
Time: July 22-25, 2026

Abstract: While most on-demand mission-critical systems are engineered to be reliable to support critical tasks, occasional failures may still occur during missions. To increase system survivability, a common practice is to abort the mission before an imminent failure. We consider optimal mission abort for a system whose deterioration follows a general three-state (normal, defective, failed) semi-Markov chain. The failure is assumed self-revealed, while the healthy and defective states have to be predicted from imperfect condition monitoring data. Due to the non-Markovian process dynamics, optimal mission abort for this partially observable system is an intractable stopping problem. For a tractable solution, we introduce a novel tool of Erlang mixtures to approximate non-exponential sojourn times in the semi-Markov chain. This allows us to approximate the original process by a surrogate continuous-time Markov chain whose optimal control policy can be solved through a partially observable Markov decision process (POMDP). We show that the POMDP optimal policies converge almost surely to the optimal abort decision rules when the Erlang rate parameter diverges. This implies that the expected cost by adopting the POMDP solution converges to the optimal expected cost. Next, we provide comprehensive structural results on the optimal policy of the surrogate POMDP. Based on the results, we develop a modified point-based value iteration algorithm to numerically solve the surrogate POMDP. We further consider mission abort in a multi-task setting where a system executes several tasks consecutively before a thorough inspection. Through a case study on an unmanned aerial vehicle, we demonstrate the capability of real-time implementation of our model, even when the condition-monitoring signals are generated with high frequency.

Biography: 

Dr. Ye received a joint B.E. in Material Science & Engineering, and Economics from Tsinghua University. He received a Ph.D. degree from National University of Singapore. He is currently an Associate Professor and Dean’s Chair in the Department of Industrial Systems Engineering & Management at National University of Singapore. His research interests include industrial statistics, reliability engineering, and data-driven operations management. His work has been published in flagship journals in statistics, reliability, and operations management, including Bernoulli, Biometrics, Biometrika, JASA, JMLR, JRSS-B, JRSS-C, Technometrics, JQT, IEEE Trans, IJOC, IISE Trans, MSOM, OR, and POMS.

■ Keynote Speaker 4

Dr. Zhaoyang Zeng, PhD,
Chief Technology Officer (CTO)
China Aero-Polytechnology Establishment, Beijing, China

Title: The Evolving Paradigm of Reliability Engineering for Complex Systems: A Review from an Uncertainty Control Perspective
Time: July 22-25, 2026

Abstract: Traditional reliability engineering is facing a fundamental crisis as aerospace systems transition toward software-intensive and autonomous architectures. This paper reviews the historical evolution of reliability through three distinct stages: the Statistical, Physics-of-Failure, and Prognostics Eras. It argues that these failure-centric frameworks are inadequate for managing the “unknown unknowns” and epistemic uncertainties inherent in modern complex systems. To address this gap, the study advocates for a paradigm shift toward the Resilience Era. Grounded in Safety-II principles, this new approach redefines the objective from minimizing failure rates to ensuring system survival under unforeseen perturbations. By transitioning from passive Uncertainty Quantification (UQ) to active Uncertainty Control (UC), the paper proposes architectural strategies such as System-Theoretic Process Analysis (STPA) and Run-Time Assurance (RTA). Finally, it defines the role of the System Resilience Architect in designing adaptive, safety-bounded autonomous systems.

Biography:

Dr. Zhaoyang Zeng is the CTO of China Aero-Polytechnology Establishment (CAPE). He has conducted extensive and pioneering systematic research in maintainability and supportability analysis, maintenance support decision-making, and effectiveness evaluation. His work has significantly advanced the innovation of equipment maintenance theories, management models, and digitalization, making vital contributions to the development of maintenance and effectiveness. Throughout his distinguished career, Dr. Zeng has received numerous prestigious honors, including the National Defense Science and Technology Progress Award and the Aviation Science and Technology Progress Award. He has published over 30 scientific papers, holds 10 authorized invention patents, and has authored or translated four monographs. Additionally, he has led or contributed to the development of more than 10 National Military Standards (GJB). Dr. Zeng also holds several prominent leadership roles within the defense and industrial sectors, serving as the Deputy Head of the Equipment Maintenance Management Professional Group and a member of the Equipment Maintenance Technology Professional Group under the Equipment Development Department of the Central Military Commission. Furthermore, he is a member of the Civil Aircraft Operational Support Professional Group of the Ministry of Industry and Information Technology, a member of the National Industrial Foundation Expert Committee.

■ Keynote Speaker 5

Zequn Wang, PhD, Chair Professor,
Associate Dean of School of Mechanical and Electrical Engineering
University of Electronic Science and Technology of China

Title: Emerging AI Methods for Reliability-Based Design Optimization: From Surrogate Modeling to Intelligent Design
Time: July 22-25, 2026

Abstract: Reliability-Based Design Optimization (RBDO) plays a central role in engineering design by seeking optimal solutions while explicitly accounting for uncertainty, variability, and failure risk. Despite its importance, conventional RBDO remains computationally demanding because it often requires repeated reliability assessments coupled with high-fidelity simulations. Recent progress in artificial intelligence, particularly in machine learning and deep learning, is opening new possibilities for overcoming these limitations and enabling faster, more scalable, and more adaptive design strategies. This talk introduces emerging AI methods for RBDO, with a focus on surrogate modeling, active learning, uncertainty quantification, and generative design exploration. It will highlight how advanced models such as Gaussian process regression, deep neural networks, physics-informed learning approaches, and multi-fidelity frameworks can improve computational efficiency while preserving accuracy in complex and high-dimensional problems. Selected examples from engineering design will be used to demonstrate how these methods support more reliable and intelligent decision-making. The talk will also explore future trends in the field, including hybrid physics-AI methods, LLMs, generative design, and autonomous optimization workflows. The presentation aims to provide a forward-looking perspective on how AI is reshaping RBDO from surrogate-assisted analysis toward intelligent design systems.

Biography:

Dr. Zequn Wang is a Professor of Mechanical Engineering and Associate Dean at the University of Electronic Science and Technology of China (UESTC). Before joining UESTC, he was an Assistant Professor in the Department of Mechanical Engineering–Engineering Mechanics at Michigan Technological University and a Postdoctoral Research Fellow in the Department of Mechanical Engineering at Northwestern University. Dr. Wang received his B.E. in 2006 and M.S. in 2009, both in Mechanical Engineering, from the University of Science and Technology Beijing, China. He earned his Ph.D. in Industrial Engineering from Wichita State University in 2014. Dr. Wang’s research focuses on developing advanced machine learning and deep learning methods for reliability-based design under uncertainty, as well as failure diagnostics and prognostics for safety-critical engineered systems. He serves as Review Editor for the Journal of Structural and Multidisciplinary Optimization and as Associate Editor for ASME VVUQ. He was a recipient of the Overseas Excellent Young Scholars award in 2023. He is also a member of the Institute of Industrial Engineers (IIE), the American Society of Mechanical Engineers (ASME), and the American Institute of Aeronautics and Astronautics (AIAA).