Recently,　a　variety　of　intelligent　structural　damage　identification　algorithms　have　been　developed　and　have　obtained　considerable　attention　worldwide　due　to　the　advantages　of　reliable　analysis　and　high　efficiency.　However,　the　performances　of　existing　intelligent　damage　identification　methods　are　heavily　dependent　on　the　extracted　signatures　from　raw　signals.　This　will　lead　to　the　intelligent　damage　identification　method　becoming　the　optimal　solution　for　actual　application.　Furthermore,　the　feature　extraction　and　neural　network　training　are　time-consuming　tasks,　which　affect　the　real-time　performance　in　identification　results　directly.　To　address　these　problems,　this　paper　proposes　a　new　intelligent　data　fusion　system　for　damage　detection,　combining　the　probabilistic　neural　network　(PNN),　data　fusion　technology　with　correlation　fractal　dimension　(CFD).　The　intelligent　system　consists　of　three　modules　(models):　the　eigen-level　fusion　model,　the　decision-level　fusion　model　and　a　PNN　classifier　model.　The　highlight　points　of　this　system　are　these　three　intelligent　models　specialized　in　certain　situations.　The　eigen-level　model　is　specialized　in　the　case　of　measured　data　with　enormous　samples　and　uncertainties,　and　for　the　case　of　confidence　level　of　each　sensor　is　determined　ahead,　the　decision-level　model　is　the　best　choice.　The　single　PNN　model　is　considered　only　when　the　data　collected　is　somehow　limited,　or　few　sensors　have　been　installed.　Numerical　simulations　of　a　two-span　concrete-filled　steel　tubular　arch　bridge　in　service　and　a　seven-storey　steel　frame　in　laboratory　were　used　to　validate　the　hybrid　system　by　identifying　both　single-　and　multi-damage　patterns.　The　results　show　that　the　hybrid　data-fusion　system　has　excellent　performance　of　damage　identification,　and　also　has　superior　capability　of　anti-noise　and　robustness.