Cheng, Y (2005) Development of bridge management systems using fuzzy case-based reasoning. PhD thesis, Kansas State University, USA.
Abstract
Case-based reasoning (CBR), one of the artificial intelligence (AI) learning approaches, is drawing the attention of many researchers in Civil Engineering. However, due to vagueness and uncertainties in knowledge representation, attribute description, similarity measure, solution transformation, case retrieval, and case inference in CBR—especially when dealing with similarity assessment—it is difficult to find the cases from a case base which are exactly the same as the query one. Therefore, fuzzy theories have been incorporated into CBR, which promises more robust, flexible, and accurate models. In this research, fuzzy case-based reasoning (FCBR) has been used to develop a model for bridge management. This model can deal with more than one objective, namely, predicting the future health condition of a bridge deck, and recommending the appropriate maintenance, rehabilitation and replacement (MR&R) actions. The FCBR model's learning capabilities have been validated using the cross-validation method. The code is implemented using the programming language C++, and all the cases used for both training and testing are extracted from the electronic bridge database of the Kansas Department of Transportation. It is shown from the experimental results that it is feasible to apply fuzzy case-based reasoning to bridge engineering and management.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Melhem, H G |
| Uncontrolled Keywords: | reasoning; replacement; artificial intelligence; bridge management; civil engineering; learning; programming; rehabilitation; training; civil engineer; experiment; case-based reasoning |
| Index terms: | artificial intelligence, testing, database, transformation, fuzzy theory, replacement, programming, validation, experiment, civil engineer, drawing, case-based reasoning, management system, reasoning |
| Subjects: | business, profession, data collection methods, programming, materials science, technical documentation, artificial intelligence, professional practice, management, professional development, cognitive psychology, data management, decision-making and optimization |
| Topics: | Roles and Professions, Research Practice, Information Management, Business Strategy, Organizational Design, Design Practice, Digital Applications, Engineering Principles |
| Descriptive scope: | 4 PCTE |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here