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Rough‐Set‐Based Knowledge Acquisition from Cases for Integrity Assessment of Bridge Structures
Author(s) -
Furuta Hitoshi,
Hirokane Michiyuki
Publication year - 1998
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/0885-9507.00105
Subject(s) - medical diagnosis , bridge (graph theory) , rough set , computer science , set (abstract data type) , knowledge acquisition , artificial intelligence , data mining , medicine , pathology , programming language
Many existing structures present an ever‐increasing demand for maintenance and repair. It is necessary in the first place to diagnose such structures correctly. The diagnoses have been dependent to date on experts of great experience. To meet the increasing demand for maintenance and repair and perform diagnoses efficiently and correctly, the development of expert systems is hoped for. This article discusses application of rough‐set theory to the acquirement of experiential knowledge contained in diagnostic cases. Rough‐set theory was applied to cases in which experts diagnosed the damage of bridges, and a minimal‐decision algorithm was derived that could make diagnoses equivalent to those of the experts. In addition, a method of deriving rules from the minimal‐decision algorithm for the construction of expert systems is discussed.