Truss　structure　is　widely　used　in　civil　engineering.　However,　it　is　difficult　to　quantitatively　monitor　the　state　of　truss　structures　because　of　the　connection　diversity　and　complexity　of　truss　structures.　In　this　paper,　electromechanical　impedance　(EMI)　technique　was　proposed　to　measure　impedance　spectra　by　using　PZT　elements　and　backpropagation　(BP)　neural　network　was　used　as　an　effective　nonlinear　conversion　tool　to　quantify　the　health　state　of　truss　structures.　Firstly,　frequency　band　of　the　spectrum　was　experimentally　determined　by　the　trial-and-error　approach.　Then　four　connection　rods　of　this　truss　structure　were　selected　for　experimental　research.　These　connection　rods　were　loosened　gradually　with　a　small　angle　increment　and　the　impedance　spectra　were　recorded.　Then,　the　measured　data　were　compressed　through　dividing　the　frequency　range　into　multiple　subbands.　And　RMSD　values　of　these　bands　showed　that　data　points　were　reduced　while　damage　features　remained.　Finally,　one　four-layered　BP　neural　network　model　was　constructed　based　on　these　compressed　data.　The　research　results　showed　that　compressed　impedance　data　could　retain　their　damage　features.　After　the　training,　the　developed　neural　network　model　could　not　only　determine　the　location　of　loosened　rod,　but　also　quantify　the　loosening　levels.