With　the　rapid　development　of　big　data,　the　Internet　of　Things　(IoT),　and　other　technological　advancements,　digital　twin　(DT)　technology　is　increasingly　being　applied　to　the　field　of　bridge　structural　health　monitoring.　Achieving　the　precise　implementation　of　DT　relies　significantly　on　a　dual-drive　approach,　combining　the　influence　of　both　physical　model-driven　and　data-driven　methodologies.　In　this　paper,　two　methods　are　proposed　to　predict　the　displacement　and　dynamic　response　of　structures　under　strong　winds,　namely,　a　Bayesian　Neural　Network　(BNN)　model　based　on　Bayesian　inference　and　a　finite　element　model　(FEM)　method　modified　based　on　genetic　algorithms　(GAs)　and　multi-objective　optimization　(MOO)　using　response　surface　methodology　(RSM).　The　characteristics　of　these　approaches　in　predicting　the　dynamic　response　of　large-span　bridges　are　explored,　and　a　comparative　analysis　is　conducted　to　evaluate　their　differences　in　computational　accuracy,　efficiency,　model　complexity,　interpretability,　and　comprehensiveness.　The　characteristics　of　the　two　methods　were　evaluated　using　data　collected　on　the　Forth　Road　Bridge　(FRB)　as　an　example　under　unusual　weather　conditions　with　strong　wind　action.　This　work　proposes　a　dual-driven　approach,　integrating　machine　learning　and　FEM　with　GNSS　and　Earth　Observation　for　Structural　Health　Monitoring　(GeoSHM),　to　bridge　the　gap　in　the　limited　application　of　dual-driven　methods　primarily　applied　for　small-　and　medium-sized　bridges　to　large-span　bridge　structures.　The　research　results　show　that　the　BNN　model　achieved　higher　R2　values　for　predicting　the　Y　and　Z　displacements　(0.9073　and　0.7969,　respectively)　compared　to　the　FEM　model　(0.6167　and　0.6283).　The　BNN　model　exhibited　significantly　faster　computation,　taking　only　20　s,　while　the　FEM　model　required　5　h.　However,　the　physical　model　provided　higher　interpretability　and　the　ability　to　predict　the　dynamic　response　of　the　entire　structure.　These　findings　help　to　promote　the　further　integration　of　these　two　approaches　to　obtain　an　accurate　and　comprehensive　dual-driven　approach　for　predicting　the　structural　dynamic　response　of　large-span　bridge　structures　affected　by　strong　wind　loading.