A　Digital　Twin　(DT)　serves　as　a　virtual　counterpart　to　real-world　entities,　including　structures,　systems,　and　components,　created　to　interpret　and　provide　insights　about　their　physical　counterpart,　the　Physical　Twin　(PT).　These　twins　are　dynamically　updated　via　sensor　networks　and　Internet　of　Things　(IoT)　technologies.　In　the　DT,　predictive　capabilities　are　enabled　through　data-driven　algorithms　and　modeling　tools.　Despite　these　advancements,　accurately　modeling　system　performance　is　complicated　by　numerous　uncertainties　present　throughout　their　lifecycle.　To　address　these　challenges,　the　Risk-Informed　Digital　Twin　(RDT)　concept　has　emerged.　RDT　integrates　probabilistic　predictions　informed　by　physics　and　data　within　the　Sustainable　Resilient　Engineering　(SRE)　framework.　SRE　extends　Performance-Based　Engineering　by　incorporating　Bayesian　methods　and　leveraging　Artificial　Intelligence　tools.　This　study　explores　the　initial　development　of　an　RDT　demonstrator,　focusing　on　the　butterfly-arch　stress-ribbon　pedestrian　bridge　in　Fuzhou,　Fujian,　China.　A　tri-axial　wireless　sensor　network　with　high　synchronization　was　installed　on　the　bridge　deck　to　capture　ambient　vibration　data.　Utilizing　Operational　Modal　Analysis,　four　distinct　finite　element　models　of　the　bridge　were　created.　These　digital　models　inform　the　prior　configurations　used　in　SRE,　enhancing　predictions　of　the　bridge＇s　performance,　particularly　for　extreme　scenarios　and　long-term　evaluations.