Bridge　risk　assessment　is　an　important　approach　to　avoiding　the　safety　accidents　of　bridges　and　ensuring　the　safety　of　the　public.　This　can　be　done　by　investigating　the　relationship　between　bridge　risks　and　bridge　criteria.　However,　such　relationship　usually　is　highly　complicated　in　actual　situations.　In　this　regard,　many　approaches　were　proposed　to　model　bridge　risks　in　the　past　decades.　Particularly,　four　alternative　approaches　including　the　artificial　neural　network　(ANN),　evidential　reasoning　with　learning　(ERL),　multiple　regression　analysis　(MRA),　and　adaptive　neuro-fuzzy　inference　system　(ANFIS)　were　deeply　analyzed　and　compared　for　bridge　risk　assessment.　However,　these　approaches　are　restricted　by　their　shortages.　Thus,　this　paper　utilizes　the　disjunctive　belief　rule-based　(DBRB)　expert　system　to　model　bridge　risks,　where　the　DBRB　expert　system　is　one　type　of　the　belief　rule-based　(BRB)　expert　system　by　considering　disjunctive　belief　rules　(DBRs)　rather　than　conjunctive　belief　rules　(CBRs)　in　a　BRB.　Furthermore,　the　dynamic　parameter　optimization　model　and　improved　differential　evolution　(IDE)　algorithm　are　proposed　to　train　the　parameters　of　the　DBRB　expert　system,　where　the　model　is　applied　to　ensure　the　completeness　of　a　DBRB　and　the　algorithm　is　used　to　get　the　global　optimal　solution.　For　justification　purpose,　two　existing　parameter　optimization　models　and　nine　alternative　models　developed　by　the　ANN,　ERL,　MRA,　and　ANFIS　are　applied　to　assess　bridge　structures.　Comparison　results　indicate　that　the　DBRB　expert　system　with　the　dynamic　parameter　optimization　model　is　better　than　those　alternative　models　and　existing　parameter　optimization　models.　©　2017　Elsevier　Ltd