Digital Twins for Risk-Informed Decision Making in Civil Engineering This workshop introduces practical methods for using digital twins to support risk-informed decisions across the lifecycle of civil infrastructure. We will define the core components of a digital twin—data pipelines, physics-based and surrogate models, uncertainty quantification, and feedback loops, and show how they combine to create a continuously updated, decision-ready representation of assets. Through case studies in structures and transportation, participants will learn how to fuse monitoring data with models to estimate evolving condition, forecast performance under extreme loads, and prioritize interventions under budget and safety constraints. Emphasis is placed on translating model outputs into clear risk metrics (e.g., failure probabilities, expected loss, resilience indicators) and on designing decision policies that are robust to uncertainty. The workshop also highlights practical considerations: data governance, interoperability, computational scalability, and validation/verification. By the end, attendees will be able to scope a fit-for-purpose digital twin, select appropriate modeling and sensing strategies, and construct defensible, transparent decision workflows for inspection planning, maintenance scheduling, and emergency response. No prior experience with specific software is required; examples will be reproducible and method-focused.
