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http://hdl.handle.net/10397/1007
| Title: | Hybrid knowledge representation in a blackboard KBS for liquid retaining structure design | Authors: | Chau, KW Albermani, F |
Issue Date: | Feb-2003 | Source: | Engineering applications of artificial intelligence, Feb. 2004, v. 17, no. 1, p. 11-18 | 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. | Keywords: | Artificial intelligence Corrosion prevention Knowledge acquisition Knowledge based systems Knowledge representation Object oriented programming Software engineering Structural design Systems analysis Liquid retaining structures |
Publisher: | Pergamon Press | Journal: | Engineering applications of artificial intelligence | ISSN: | 0952-1976 | EISSN: | 1873-6769 | 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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|---|---|---|---|---|
| EAAI1.pdf | Pre-published version | 168.25 kB | Adobe PDF | View/Open |
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