Conventional　evaluation　of　the　overall　mechanical　properties　and　ultimate　flexural　capacity　of　prestressed　hollow　core　slabs　after　a　fire　exposure　depends　heavily　on　the　inversion　of　fire　scene　temperature.　To　avoid　this　drawback,　this　paper　presents　a　new　methodology　which　combines　a　generalized　regression　neural　network　(GRNN)　with　conventional　non-destructive　testing　technology.　Thereby,　a　neural　network　model　for　predicting　the　material　performance　parameters　after　fire　exposure　is　obtained　based　on　conventional　testing　indices.　A　hollow　core　slab　bridge　is　used　as　an　example,　and　the　applicability　of　the　trained　network　model　is　confirmed　using　numerical　simulation　and　a　field　failure　test.　Results　show　that　the　overall　relative　error　of　GRNN　in　predicting　the　key　performance　parameters　of　the　bridge　after　fire　exposure　is　less　than　10%.　Further,　because　of　the　good　thermal　inertia　of　the　concrete,　the　relative　error　in　predicting　the　material　performance　parameters　of　steel　after　a　fire　is　less　than　5%.　Moreover,　the　ultimate　flexural　capacity　of　the　prestressed　hollow　core　slab　after　a　fire　can　be　accurately　evaluated　by　feeding　the　material　performance　parameters　predicted　by　GRNN　neural　network　into　the　finite　element　(FE)　model.