Advanced Structural Reliability Methods and Probabilistic Digital Twins

Recent advances in probabilistic engineering mechanics, advanced structural reliability methods, structural health monitoring, sensing technologies, large-scale data infrastructures, and machine learning are opening a new era for the intelligent design, assessment, and integrity management of structures and infrastructure systems. At the same time, climate change, aging assets, increasing system interdependencies, and rising exposure to cascading and compound crises are creating an urgent need for rational and scientifically grounded approaches to reliability assessment, risk modelling, and decision support.

This course provides a coherent introduction to probabilistic modelling and analysis for design and integrity management, with particular emphasis on the fusion of structural reliability theory, risk and systems modelling, Bayesian updating, decision analysis, structural health monitoring, and probabilistic digital twins. A central objective is to show how advanced reliability and risk methods can connect probabilistic representations of loads, resistance, deterioration, and structural response with monitoring data, system-level risk characterizations, and decisions on design, inspection, maintenance, and retrofit.

The course is designed to bridge foundational theory and emerging applications by integrating probabilistic engineering mechanics with systems risk thinking and data-informed integrity management. It highlights how modern sensing and data technologies can be combined with Bayesian methods, simulation-based reliability analysis, and value-of-information concepts to support resilient, adaptive, and risk-informed management of infrastructure.

There is increasing international interest in these topics across research, engineering consultancy, infrastructure ownership, asset management, and public-sector governance.

Participants will gain a structured overview of the concepts, analytical methods, and technological developments that are shaping the next generation of reliability-based design, system risk assessment, and intelligent integrity management.

The course which is endorsed by the Joint Committee on Structural Safety (JCSS), will take place over 4 full days and will bring together participants from different parts of the world, including researchers, doctoral students, practicing engineers, consultants, infrastructure owners and operators, and professionals working with reliability assessment, risk analysis, monitoring, and integrity management of structures and infrastructure systems.

The course is organized around four closely connected themes. The first day is devoted to probabilistic modelling and introduces the foundations needed for reliability-based design and assessment. Topics include uncertainty and Bayesian probability theory, probabilistic models of loads, resistances, and structural responses, and the representation of uncertainty by random variables, stochastic processes, and random fields.

The second day focuses on advanced probabilistic analysis. Building on the modelling framework from Day 1, participants are introduced to the principal methods of structural reliability analysis, including FORM and SORM, Monte Carlo simulation, subset simulation, and the Probability Density Evolution Method, together with discussion of their strengths, limitations, and fields of application.

The third day extends the perspective from component and structural reliability to systems modelling, system characteristics, and risk-informed decision making. This part of the course addresses systems representations, the characterization of risk, robustness, resilience, viability, and sustainability, and the use of Bayesian decision analysis and Value-of-Information concepts to support rational decisions on design, inspection, maintenance, and integrity management.

The fourth day is devoted to probabilistic digital twins and their role in modern integrity management. Here the course addresses conceptual frameworks for probabilistic digital twins, structural health monitoring, sensing strategies, feature extraction, probabilistic synthetic representations of structural performance, machine learning for classification, and condition monitoring for decision support.

Taken together, the course provides a coherent introduction to the fusion of probabilistic engineering mechanics, advanced reliability analysis, systems risk modelling, Bayesian decision analysis, structural health monitoring, and data-driven technologies. The overall objective is to show how these domains can be integrated into rational frameworks for design and integrity management of structures and infrastructure systems under uncertainty.

The program for the course is presented in the course plan section. The final announcement of the course, including practical information, will be distributed 3 months ahead of the course.