The　belief　rule-base　(BRB)　inference　methodology,　which　uses　the　evidential　reasoning　(RIMER)　approach,　has　been　widely　popular　in　recent　years.　As　an　expert-system　methodology　using　the　RIMER　approach,　BRB　is　used　for　storing　various　types　of　uncertain　knowledge　in　the　form　of　belief　structure.　Several　structure-learning　approaches　have　been　proposed　in　recent　years.　However,　these　approaches　are　deficient　in　various　aspects,　do　not　have　repeatability,　hold　incomplete　data,　and　are　constrained　by　the　associated　scale-utility　value.　Moreover,　considering　the　influence　of　the　number　of　rules　for　a　BRB　system,　two　scenarios　are　designed　to　reveal　the　relationship　between　structure　feature　and　fewer/excessive　rules.　Excessive　rules　may　lead　to　a　BRB　that　is　equipped　with　an　over-complete　structure,　whereas　significantly　fewer　rules　may　result　in　a　BRB　with　an　incomplete　structure.　To　solve　these　problems,　we　initially　proposed　to　develop　an　adjusted　structure　that　is　leading　to　the　establishment　of　a　complete　structure　instead　of　incomplete　and　over-complete　structures.　By　scenario　analysis　and　experimental　verification　through　parameter　learning　of　BRBs,　we　summarize　several　features　of　two　scenarios,　which　can　be　used　to　reveal　certain　number　of　key　BRB　properties.　Finally,　density　and　error　analyses　are　introduced　to　dynamically　prune　or　add　rules　to　construct　the　complete　structure,　particularly　that　of　the　BRB　comprising　multiple-antecedent　attributes.　We　verify　the　effectiveness　of　the　proposed　approach　by　testing　its　use　in　a　practical　case　study　on　oil　pipeline-leak　detection　and　demonstrate　how　the　approach　can　be　implemented.　(C)　2016　Elsevier　B.V.　All　rights　reserved.