Deep　learning　is　extensively　utilised　in　transport　geotechnical　engineering.　However,　deep　architectures　have　large　computational　costs　and　update　times,　while　failing　to　understand　decisions.　To　this　regard,　we　propose　an　interpretable　dynamic　broad　network　combined　with　ground-penetrating　radar　for　internal　defect　identification　in　roadbeds.　The　method　is　more　suitable　for　feature　characterisation　of　two-dimensional　data　and　satisfies　incremental　updates.　The　test　results　indicated　that　the　proposed　method　has　an　average　recognition　accuracy　of　0.9124　for　the　four　types　of　internal　defects　in　roadbeds.　Compared　to　the　other　four　classical　machine　learning　methods,　it　balances　training　efficiency　and　recognition　accuracy.　Robustness　analysis　results　demonstrated　that　the　method　is　noise-resistant.　However,　comprehending　the　recognition　results　of　intelligent　algorithms　is　a　key　topic.　Local　interpretation　approach　is　introduced　to　quantify　the　feature　importance　that　affects　the　decision　of　the　model.　Based　on　the　feature　importance　calculation,　it　is　possible　to　distinguish　between　positive　and　negative　regions　in　one　sample　that　influence　the　decision　of　the　detection　model.　These　interpretative　analyses　can　assist　us　in　better　understanding　the　reasons　for　decisions　generated　by　the　detection　model　that　provide　technical　support　for　subsequent　enhancements.