Most　of　existing　time　domain　methods　fail　to　identify　the　unknown　structural　parameter　in　the　ambient　con-dition,　because　the　force　location　is　unknown　and　the　number　of　unknown　forces　is　usually　larger　than　the　number　of　sensors.　A　novel　output-only　structural　identification　method　based　on　the　random　decrement　(RD)　technique　and　covariance　matrix　adaptation　evolutionary　strategy　(CMA-ES)　is　presented.　The　RD　functions　of　a　structure　under　ambient　excitation　are　extracted　and　treated　as　free　vibration　responses　for　structural　parameter　identification.　The　unknown　structural　parameters　are　identified　by　minimizing　the　discrepancy　between　the　measured　and　calculated　RD　functions　with　the　CMA-ES.　Unlike　traditional　output-only　methods,　which　require　the　estimation　of　the　input　excitation　in　the　process,　the　proposed　method　does　not　require　the　identification　of　the　input　forces,　thereby　significantly　reducing　the　computational　cost.　Numerical　studies　on　two-and　three-dimensional　trusses　and　experimental　studies　on　a　steel　frame　are　presented　to　demonstrate　the　accuracy　and　efficiency　of　the　proposed　approach.　The　effects　of　measurement　noise　and　measurement　duration　on　the　identified　results　are　examined.　Results　show　that　the　proposed　method　can　obtain　satisfactory　results　even　with　20%　measurement　noise　and　structural　damage　less　than　10%　can　be　detected.　Comparison　studies　with　the　genetic　algorithm　(GA)　and　particle　swarm　optimization　(PSO)　are　presented　to　verify　the　computational　effi-ciency　of　the　proposed　method.　Results　also　show　that　the　proposed　CMA-ES　has　a　faster　convergence　speed　than　the　GA　and　PSO　method.　The　results　demonstrate　that　the　proposed　technique　can　be　used　to　detect　damage　severity　and　location　in　structures　under　ambient　conditions.