Motivation

The Digital Twin (DT) is a virtual replica of buildings, processes, structures, people, systems created and maintained in order to answer questions about its physical part, the Physical Twin (PT). In the case of the built environment, the PT may be represented by buildings, bridges, offshore structures. Full synchronization between the DT and the PT will provide a perpetual learning process and updating between the two twins. However, multiple sources of uncertainty during the lifecycle challenge our prediction capabilities. It follows the significance of the Risk-Informed Digital Twin (RDT) where tools of data-driven Uncertainty Quantification in all its facets (probabilistic, non-probabilistic or hybrid), Risk Analysis and decision making under uncertainty are fully integrated.

Objective

The scope of this Special Session is to bring together expert practitioners, researchers and academics to develop methods, frameworks and tools in this broad area, including but not limited to Structural Health Monitoring, Value of Information, Uncertainty Quantification (probabilistic approaches, interval model, fuzzy sets, Bayesian model, imprecise probabilities, probabilistic sensitivity analysis), surrogate modelling using active learning technologies, structural reliability, stochastic dynamic analysis, machine learning, reinforcement learning, lifecycle optimal design and management under uncertainty. Contributions addressing practical applications are also encouraged, e.g. buildings, bridges, offshore, geotechnical systems, up to urban systems and regional scale analysis.