Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1007
Title: Hybrid knowledge representation in a blackboard KBS for liquid retaining structure design
Authors: Chau, KW 
Albermani, F
Keywords: Artificial intelligence
Corrosion prevention
Knowledge acquisition
Knowledge based systems
Knowledge representation
Object oriented programming
Software engineering
Structural design
Systems analysis
Liquid retaining structures
Issue Date: Feb-2003
Publisher: Elsevier
Source: Engineering applications of artificial intelligence, Feb. 2004, v. 17, no. 1, p. 11-18 How to cite?
Journal: Engineering applications of artificial intelligence 
Abstract: This paper highlights the importance of design expertise, for designing liquid retaining structures, including subjective judgments and professional experience. Design of liquid retaining structures has special features different from the others. Being more vulnerable to corrosion problem, they have stringent requirements against serviceability limit state of crack. It is the premise of the study to transferring expert knowledge in a computerized blackboard system. Hybrid knowledge representation schemes, including production rules, object-oriented programming, and procedural methods, are employed to express engineering heuristics and standard design knowledge during the development of the knowledge-based system (KBS) for design of liquid retaining structures. This approach renders it possible to take advantages of the characteristics of each method. The system can provide the user with advice on preliminary design, loading specification, optimized configuration selection and detailed design analysis of liquid retaining structure. It would be beneficial to the field of retaining structure design by focusing on the acquisition and organization of expert knowledge through the development of recent artificial intelligence technology.
URI: http://hdl.handle.net/10397/1007
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2003.11.007
Rights: Engineering Applications of Artificial Intelligence © 2003 Elsevier Ltd. The journal web site is located at http://www.sciencedirect.com.
Appears in Collections:Journal/Magazine Article

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