Bridge　risks　are　often　evaluated　periodically　so　that　the　bridges　with　high　risks　can　be　maintained　timely.　This　paper　develops　an　adaptive　neuro-fuzzy　system　(ANFIS)　using　506　bridge　maintenance　projects　for　bridge　risk　assessment,　which　can　help　Highways　Agency　to　determine　the　maintenance　priority　ranking　of　bridge　structures　more　systematically,　more　efficiently　and　more　economically　in　comparison　with　the　existing　bridge　risk　assessment　methodologies　which　require　a　large　number　of　subjective　judgments　from　bridge　experts　to　build　the　complicated　nonlinear　relationships　between　bridge　risk　score　and　risk　ratings.　The　ANFIS　proves　to　be　very　effective　in　modelling　bridge　risks　and　performs　better　than　artificial　neural　networks　(ANN)　and　multiple　regression　analysis　(MRA).　©　2007　Elsevier　Ltd.　All　rights　reserved.