A　roller　compacted　concrete　dam　(RCCD)　located　in　Cambodia　has　been　gradually　deformed　over　the　operation　period　(2011-2019),　and　the　creep　effect　of　the　dam　foundation　is　significant.　In　order　to　make　integrity　and　safety　assessments　of　the　dam,　it　is　necessary　to　know　the　actual　mechanical　properties　of　the　foundation.　This　research　proposes　an　intelligent　computational　framework　for　analysing　the　time-dependent　working　behaviour　of　the　RCCD　combined　with　viscoelastic　finite　element　methods　and　advanced　software　techniques.　According　to　the　long-term　deformation　characteristics　of　the　foundation,　the　Burgers　model　is　employed　to　describe　the　constitutive　relation　of　the　bedrock.　A　finite　element　formulation　describes　the　relationship　between　dam　deformation　and　mechanical　properties　in　the　creep　regime.　A　structural　inverse　methodology　based　on　improved　parallel　grey　wolf　optimization　(IGWO)　is　developed　in　order　to　search　and　identify　viscoelastic　parameters　of　the　dam　foundation.　The　nonlinear　convergence　factor　strategy　and　multi-core　parallel　computing　are　introduced　to　enhance　global　search　capability　and　improve　the　accuracy　of　the　optimization　algorithm.　An　example　of　analysis　is　performed　on　a　dam　section,　and　the　results,　which　are　compared　with　actual　measurements　for　discussion,　demonstrate　that　the　selected　constitutive　model　is　reasonable　and　the　designed　inverse　methodology　is　feasible.　Moreover,　the　proposed　IGWO　algorithm　is　very　competitive　with　other　state-of-the-art　optimization　methods　such　as　basic　grey　wolf　optimization　(GWO),　particle　swarm　optimization　(PSO)　and　whale　optimization　algorithm　(WOA)　for　parameter　inversion　and　real-time　problems.