Truck　cranes　are　indispensable　heavy-duty　loading　and　unloading　equipment　in　industrial　production.　A　boom　is　the　key　load-bearing　component　of　a　truck　crane,　and　its　health　has　a　vital　influence　on　a　crane＇s　lifting　performance　and　safety　in　production.　Therefore,　it　is　urgent　to　develop　a　structural　health　monitoring　(SHM)　method　for　a　boom　structure.　In　this　research,　an　improved　intelligent　defect　location　algorithm　based　on　helical　guided　waves　is　applied　to　the　defect　detection　of　a　U-shaped　boom.　The　improved　intelligent　algorithm　is　a　defect　location　algorithm　based　on　the　ellipse　imaging　principle,　which　combines　an　evolutionary　algorithm　with　a　K-means　algorithm　and　can　identify　the　location　of　defects　through　the　distribution　of　individuals.　According　to　the　propagation　characteristics　of　the　helical　guided　waves　in　the　structure　of　a　U-shaped　boom,　an　optimization　scheme　for　the　improved　intelligent　defect　location　algorithm　is　proposed　that　fully　considers　the　fact　that　the　group　velocity　of　the　helical　guided　waves　varies　with　the　wall　thickness,　and　the　corresponding　group　velocity　is　used　to　accurately　calculate　the　arrival　time　of　each　initial　individual　to　improve　the　accuracy　of　the　defect　location.　Numerical　simulations　and　experiments　are　conducted　to　verify　the　effectiveness　of　the　proposed　improved　intelligent　defect　location　algorithm　in　the　defect　location　algorithm.　When　only　one　group　velocity　is　considered,　the　improved　intelligent　defect　location　algorithm　is　in　good　agreement　with　the　elliptical　imaging　algorithm.　When　the　group　velocities　for　different　wall　thicknesses　are　fully　considered,　the　detection　results　of　the　optimized　intelligent　defect　location　algorithm　have　a　higher　resolution.　This　optimization　algorithm　provides　a　tool　for　the　SHM　of　a　U-shaped　boom　based　on　guided　waves.