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A platform for research: civil engineering, architecture and urbanism
Knowledge acquisition and representation model for developing an expert system for pavement maintenance and REhabilitation strategy in the State of Ohio (ESPRESSO)
In pavement management, the decision making for maintenance and rehabilitation strategy is a paramount factor. When making a decision, experts usually select the best strategy by a reasoning process based on heuristic and experiential subjective judgment. In this paper, the expert system for pavement maintenance and rehabilitation strategy in the state of Ohio (ESPRESSO) is developed. The development of ESPRESSO consists of three phases: knowledge acquisition, knowledge representation, and knowledge base. The first phase for developing ESPRESSO is to acquire knowledge from a literature review and experts. For this purpose a knowledge acquisition model is specially designed and systematically used to facilitate this task. In the second phase, decision tree structures are selected to represent the expert knowledge. The decision trees consist of three levels: the evaluation of pavement condition, the M&R strategy for pavement condition, and the M&R strategy for pavement structure. Evaluation of pavement condition encompasses the types, distresses, pavement condition rating, and structural deduct factors. The maintenance and rehabilitation strategy consist of major and minor rehabilitation and maintenance. The decision tree models were selected to represent the expert knowledge, because these models could simulate the path and pattern of the experts' thought process. In this paper, the knowledge acquisition and representation phases for developing ESPRESSO are presented in detailed.
Knowledge acquisition and representation model for developing an expert system for pavement maintenance and REhabilitation strategy in the State of Ohio (ESPRESSO)
In pavement management, the decision making for maintenance and rehabilitation strategy is a paramount factor. When making a decision, experts usually select the best strategy by a reasoning process based on heuristic and experiential subjective judgment. In this paper, the expert system for pavement maintenance and rehabilitation strategy in the state of Ohio (ESPRESSO) is developed. The development of ESPRESSO consists of three phases: knowledge acquisition, knowledge representation, and knowledge base. The first phase for developing ESPRESSO is to acquire knowledge from a literature review and experts. For this purpose a knowledge acquisition model is specially designed and systematically used to facilitate this task. In the second phase, decision tree structures are selected to represent the expert knowledge. The decision trees consist of three levels: the evaluation of pavement condition, the M&R strategy for pavement condition, and the M&R strategy for pavement structure. Evaluation of pavement condition encompasses the types, distresses, pavement condition rating, and structural deduct factors. The maintenance and rehabilitation strategy consist of major and minor rehabilitation and maintenance. The decision tree models were selected to represent the expert knowledge, because these models could simulate the path and pattern of the experts' thought process. In this paper, the knowledge acquisition and representation phases for developing ESPRESSO are presented in detailed.
Knowledge acquisition and representation model for developing an expert system for pavement maintenance and REhabilitation strategy in the State of Ohio (ESPRESSO)
KSCE J Civ Eng
Wee, Seong-Dong (author)
KSCE Journal of Civil Engineering ; 2 ; 315-333
1998-09-01
19 pages
Article (Journal)
Electronic Resource
English
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