As　a　key　load-bearing　component　of　the　crane,　the　health　status　of　the　boom　directly　affects　the　performance　and　safety　of　the　crane.　Therefore,　it　is　especially　important　to　develop　a　structural　health　monitoring　(SHM)　method　for　the　boom.　In　this　study,　a　compact　array　intelligent　location　algorithm　based　on　combined　time-of-flight　method　(CTFM)　is　proposed　and　applied　to　the　defect　detection　of　U-shaped　boom.　In　this　algorithm,　direct　time-of-flight　(DTFM)　and　time-of-flight　difference　(TFDM)　are　combined　to　build　a　scattering　point　evaluation　model　to　screen　scattering　point,　and　evolutionary　strategies　and　clustering　algorithms　are　used　to　search　and　locate　scattering　points　quickly.　The　algorithm　transforms　the　classical　imaging　problem　into　the　estimation　and　search　scattering　points,　and　uses　individual　distribution　to　identify　the　defect　position.　First　of　all,　the　numerical　simulation　proves　that　the　proposed　intelligent　location　algorithm　has　reliable　performance　in　detecting　defects　of　different　shapes　and　sizes.　Compared　with　the　traditional　ellipse　imaging　algorithm,　the　compact　array　intelligent　location　algorithm　based　on　CTFM　shows　advantages　in　positioning　resolution,　positioning　accuracy　and　algorithm　execution　efficiency.　Subsequently,　through　the　analysis　of　the　experimental　signals　of　doublehole　defect,　it　is　further　verified　that　the　algorithm　can　not　only　improve　the　defect　positioning　accuracy　and　algorithm　execution　efficiency,　but　also　effectively　remove　the　interference　of　direct　waves　by　adjusting　parameters.　Finally,　the　influence　of　parameter　setting　on　the　detection　results　is　studied,　and　the　optimal　parameter　setting　of　the　algorithm　is　discussed.　This　algorithm　provides　a　tool　for　multi-defect　detection　of　U-shaped　boom.