Structural　health　monitoring　(SHM)　of　bridges　and　other　building　structures　has　been　widely　concerned　by　researchers　and　engineers.　SHM　achieved　a　breakthrough　by　using　convolutional　neural　networks　(CNNs)　in　deep　learning.　However,　the　traditional　vision　based　SHM　can　not　monitor　the　invisible　structural　damage.　The　traditional　vibration　based　SHM　needs　to　convert　1D　signals　into　2D　images,　which　is　easy　to　lose　information　in　the　conversion　process,　and　requires　a　large　scale　of　hardware　circuit.　In　this　paper,　a　1D　in-situ　convolution　system　(ICS)　based　on　vibration　signal　is　proposed.　The　SHM　is　realized　by　1D　convolution　calculation　of　the　structural　vibration　signal　detected　by　a　high-sensitivity　triboelectric　nanogenerator　(TENG)　through　one　in-situ　convolution　transistor.　Based　on　the　natural　adaptability　of　TENG　to　1D　convolution　calculation,　and　combined　with　the　ability　of　a　single　in-situ　convolution　device　to　replace　more　than　eight　multi　conductance　devices,　ICS　can　reduce　the　required　SHM　computing　array　area　by　over　87.5%.　Finally,　the　real-time　identification　of　different　positions　and　different　damage　levels　of　the　steel　bridge　was　realized　by　simulating　the　vibration　response　of　the　steel　bridge.　©　2024　Elsevier　Ltd