It　is　proposed　in　this　paper　a　novel　damage　identification　method　of　concrete-filled　steel　tubular　arch　bridge　using　data　fusion　based　on　information　allocation　theory.　This　method　is　actually　a　data　fusion　process,　in　which　the　statistic　characteristics　of　the　measured　data,　such　as　mean,　standard　deviation　are　calculated　firstly,　and　then　the　bad　points　are　excluded　by　the　Grubbs　Guidelines　from　the　data　set.　After　that,　the　measured　data　satisfied　to　certain　precision　are　sent　to　a　data　fusion　centre　where　the　fusion　computation　is　performed　by　employing　a　weighted　average　algorithm,　and　the　damage　location　of　a　CFST　arch　bridge　is　determined　by　the　peak　value　method　finally.　The　efficiency　and　feasibility　of　the　method　proposed　is　validated　by　detecting　both　single-　and　multi-damage　patterns　of　a　two-span　CFST　arch　bridge,　and　the　effect　of　environmental　on　identification　results　is　studied　simultaneously.　The　results　show　that　the　proposed　method　is　capable　of　detecting　damage　and　has　a　good　robustness.　©　2011　Published　by　Elsevier　Ltd.