Delamination　is　a　frequent　failure　mode　of　laminated　fiber-reinforced　polymer　(FRP)　composites　structure　in　aeronautical　and　other　industries,　leading　to　changes　in　vibration　characteristics.　Vibration-based　techniques　evaluate　and　compare　the　dynamic　response　between　damaged　and　undamaged　structures,　and　guarantee　the　non-destructive　measurement　with　reliability　and　repeatability.　Previous　studies　typically　concentrate　on　using　finite　element　method　to　obtain　vibration　characteristics　and　enhance　the　database　for　intelligent　algorithms.　This　paper　presents　a　semi-analytical　result　using　the　Chebyshev-Ritz　method　to　expand　delamination　prediction.　Vibration　frequency　serves　as　a　global　damage　indicator,　and　multi-order　frequency　characteristics　are　utilized　to　identify　the　delamination　length　and　location　of　FRP　composite　plates.　A　database　of　natural　frequencies　corresponding　to　damage　parameters　for　FRP　laminated　plates　is　generated　based　on　the　established　model　using　the　region　approach.　An　intelligent　approach,　known　as　a　genetic　algorithm　optimization-based　back-propagation　(GA-BP)　artificial　neural　network,　is　utilized　for　system　identification.　The　network　model　is　subjected　to　a　sensitivity　analysis,　where　artificial　noise　is　added　to　vibration　frequency　to　distinguish　between　the　actual　structure　and　the　numerical　model.　The　results　indicate　that　the　GA-BP　algorithm　shows　good　accuracy　and　stable　performance　against　the　standard　neural　networks　for　delamination　analysis.