Road　tunnels　are　critical　infrastructures　that　require　regular　maintenance　and　inspection　to　ensure　the　safety　of　users.　However,　traditional　inspection　methods　can　be　costly,　time-consuming,　and　may　not　provide　an　objective　assessment　of　the　tunnel＇s　condition.　To　overcome　these　challenges,　researchers　have　recently　developed　newly　promising　approaches　that　combine　tunnel＇s　indirect　testing,　e.g.　ground-penetrating　radar　(GPR),　with　the　potentialities　of　artificial　intelligence　(AI).　This　innovative　technique　allows　for　the　automatic　detection　and　classification　of　defects　in　tunnel　linings,　providing　a　faster,　more　efficient,　and　more　reliable　method　for　assessing　the　health　of　road　tunnels.　This　study　explores　the　application　of　one　of　the　state-of-the-art　deep　learning　models,　i.e.　the　compact　convolution　transformer　(CCT),　to　classify　defects　in　GPR　images　of　tunnel　linings.　The　dataset　used　in　this　study　consists　of　inspections　of　tunnels　built　between　the　1960s　and　1980s.　Two　CCT　models　were　trained　on　filtered　and　unfiltered　datasets　and　then　compared　to　assess　the　effects　of　noise　on　the　identification　task.　©　2023　IEEE.