Structural　health　monitoring　(SHM)　is　a　kind　of　data-intensive　applications　for　wireless　sensors　network　(WSN),　which　usually　requires　a　high　network　capacity.　However,　the　bandwidth　of　traditional　single-radio　single-channel　(SR-SC)　WSN　is　quite　limited.　In　order　to　meet　the　requirement　of　structural　monitoring,　we　investigate　the　multi-radio　multi-channel　multi-power　(MR-MC-MP)　communication　to　improve　the　data　collection　performance　of　SHM-oriented　WSNs　in　terms　of　network　capacity　and　power　consumption.　First,　the　data　collection　problem　in　MR-MC-MP　WSNs　is　modeled　as　an　optimization　problem　under　the　constraint　of　available　time　slots,　radios,　channels,　and　power　levels.　And　then,　combining　the　fast　convergence　of　the　particle　swarm　optimization　(PSO)　algorithm　and　high　exploration　performance　of　flower　pollination　optimization　(FPA)　algorithm,　we　propose　a　novel　binary　hybrid　meta-heuristic　algorithm　named　BFPA-PSO　to　solve　the　problem.　In　order　to　verify　the　advantage　of　the　proposed　BFPA-PSO,　some　other　meta-heuristic　algorithms　are　tested　for　the　problem　as　well.　Finally,　several　simulation　experiments　are　carried　out　to　test　and　compare　the　performance　of　different　algorithms.　Experiment　results　demonstrate　that　the　proposed　BFPA-PSO　algorithm　has　superior　performance　in　terms　of　network　capacity　and　energy　consumption.