In　this　paper,　a　novel　approach　to　model　updating　for　a　large-scale　railway　bridge　using　orthogonal　diagonalization　(OD)　coupled　with　an　improved　particle　swarm　optimization　(IPSO)　is　proposed.　Particle　swarm　optimization　(PSO)　is　a　well-known　and　widely　applied　evolutionary　algorithm.　However,　as　other　evolutionary　algorithms　(EAs),　PSO　has　two　main　drawbacks　that　may　reduce　its　capability　to　tackle　optimization　issues.　A　fundamental　shortcoming　of　PSO　is　premature　convergence.　On　the　other　hand,　since　PSO　employs　all　populations　to　seek　the　best　solution　through　iterations,　it　is　very　time-consuming.　This　makes　PSO　as　well　as　EAs　difficult　to　apply　for　optimization　problems　of　large-scale　structural　models.　In　order　to　overcome　those　drawbacks,　we　propose　coupling　OD　with　IPSO　(ODIPSO).　OD　is　applied　to　arrange　the　position　of　particles　and　to　select　only　particles　with　the　best　solution　for　next　iterations,　which　helps　to　reduce　the　computational　cost　dramatically.　There　are　several　significant　features　of　ODIPSO:　(1)　IPSO　is　employed　to　tackle　the　problem　of　premature　convergence　of　PSO;　(2)　only　one　guide　is　used　to　update　the　velocity　of　particles　instead　of　utilizing　both　guides,　consisting　of　the　local　best　and　the　global　best;　and　(3)　in　each　iteration,　only　the　velocity　and　the　position　of　the　best　particles　are　updated.　In　order　to　assess　the　effectiveness　of　the　proposed　approach,　a　large-scale　railway　bridge　calibrated　on　the　field　is　employed.　This　paper　also　introduces　the　use　of　wireless　triaxial　sensors　(replacing　classical　wired　systems)　to　obtain　structural　dynamic　characteristics.　The　appearance　of　wireless　triaxial　transducers　increases　significantly　the　freedom　in　designing　an　ambient　vibration　test.　The　results　show　that　ODIPSO　not　only　outperforms　PSO,　IPSO　and　OD　combined　with　PSO　(ODPSO)　in　terms　of　accuracy,　but　also　dramatically　reduces　the　computational　time　compared　to　PSO　and　IPSO.　©　2020　Elsevier　Ltd