Stochastic　model　updating　provides　an　effective　way　of　handling　uncertainties　existing　in　real-world　structures.　In　general,　probabilistic　theories,　fuzzy　mathematics　or　interval　analyses　are　involved　in　the　solution　of　inverse　problems.　However　in　practice,　probability　distributions　or　membership　functions　of　structural　parameters　are　often　unavailable　due　to　insufficient　information　of　a　structure.　At　this　moment　an　interval　model　updating　procedure　shows　its　superiority　in　the　aspect　of　problem　simplification　since　only　the　upper　and　lower　bounds　of　parameters　and　responses　are　sought.　To　this　end,　this　study　develops　a　new　concept　of　interval　response　surface　models　for　the　purpose　of　efficiently　implementing　the　interval　model　updating　procedure.　The　frequent　interval　overestimation　due　to　the　use　of　interval　arithmetic　can　be　maximally　avoided　leading　to　accurate　estimation　of　parameter　intervals.　Meanwhile,　the　establishment　of　an　interval　inverse　problem　is　highly　simplified,　accompanied　by　a　saving　of　computational　costs.　By　this　means　a　relatively　simple　and　cost-efficient　interval　updating　process　can　be　achieved.　Lastly,　the　feasibility　and　reliability　of　the　developed　method　have　been　verified　against　a　numerical　mass-spring　system　and　also　against　a　set　of　experimentally　tested　steel　plates.　©　2015　Elsevier　Ltd.　All　rights　reserved.