High　data　transmission　and　storage　cost　caused　by　massive　data　collected　is　a　critical　problem　in　bridge　structural　health　monitoring.　Thus　the　compressed　sensing　theory　is　introduced　in　this　paper　to　reduce　the　sampling.　The　conventional　measurement　matrix　is　optimized　to　reduce　the　coherence　between　the　measurement　matrix　and　sparse　basis,　which　is　benefit　for　accurately　reconstructing　the　original　dynamic　responses　with　a　limited　sampling　data.　The　field　test　data　under　ambient　vibration　of　Ji＇an　Bridge　is　utilized　to　verify　the　feasibility　and　effectiveness　of　the　proposed　structural　dynamic　response　reconstruction　method　for　bridges　based　on　compressed　sensing.　The　studied　results　include:　the　reconstructed　response　based　on　compressed　sensing　agrees　well　with　the　original　response　in　time　domain;　when　the　compression　ratio　is　more　than　20%,　the　relative　errors　of　the　reconstructed　response　maintain　less　than　10%;　the　reconstructed　responses　using　optimized　measurement　matrix　provide　higher　accuracy　than　those　using　initial　measurement　matrix,　especially　in　the　case　of　lower　compression　ratio,　resulting　in　reduction　of　data　collection;　the　spectrum　of　the　reconstructed　response　using　optimized　measurement　matrix　is　smoother　and　matches　well　with　the　original　response　spectrum,　and　the　peaks　of　the　spectrum　can　be　picked　accurately;　in　contrast,　the　spectrum　of　the　reconstructed　response　without　optimization　of　the　measurement　matrix　has　more　misjudgment　of　peaks,　and　some　peaks　even　cannot　be　identified;　the　measurement　matrix　optimization　method　can　be　applied　to　random　Gaussian　matrix,　Bernoulli　matrix　and　sparse　random　matrix.　The　results　demonstrate　that　the　proposed　structural　dynamic　response　reconstruction　method　for　bridges　based　on　compressed　sensing　is　an　effective　way　to　accurately　reconstruct　original　dynamic　response　with　a　limited　sampling　data.　©　2022,　Editorial　Board　of　Journal　of　Vibration　Engineering.　All　right　reserved.