Based　on　the　nerve　cell　model,　a　new　BP　algorithm　with　weight　and　threshold　synchro-learning　ability　is　developed.　The　weight　and　threshold　of　the　nerve　cell　model　is　considered　as　the　adaptive　learning　variables　in　this　new　BP　algorithm　and　are　adjusted　synchronously　in　the　learning　process,　which　improves　the　behavior　of　the　traditional　BP　algorithm.　The　numerical　analysis　of　the　structural　damage　detection　shows　that　this　new　BP　algorithm　provides　a　faster　convergence　to　the　solution　and　a　higher　accuracy　of　the　detection,　which　is　an　improvement　to　the　traditional　BP　algorithm　with　slower　convergence　and　tendency　to　over　fitting.