| Download | - View accepted manuscript: Fuzzy expert system to assess corrosion of cast/ductile iron pipes from backfill properties (PDF, 862 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1111/j.1467-8667.2005.00417.x |
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| Author | Search for: Najjaran, H.1; Search for: Sadiq, R.1; Search for: Rajani, B. B.1 |
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| Affiliation | - National Research Council of Canada. NRC Institute for Research in Construction
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| Format | Text, Article |
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| Subject | corrosion, soil corrosivity, fuzzy modeling, expert system, pipe deterioration, point-scoring method; Cast iron |
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| Abstract | Several factors may contribute to the structural failure of cast and ductile iron water mains, the most important of which is considered to be corrosion. The ANSI/AWWA C105/A21.5?99 10-point scoring (10-P) method is commonly used to predict corrosivity potential of a given soil sample using certain soil properties. The 10-P and other scoring methods use binary logic to classify the soil either as corrosive or non-corrosive.Fuzzy logic extends binary logic in this context as it recognizes the real world phenomena using a certain degree of membership between 0 and 1. This paper presents a fuzzy logic expert system capable of predicting the deterioration of cast and ductile iron water mains based on surrounding soil properties. The proposed model consists of two modules: a knowledge base and an inference mechanism. The knowledge base provides information for better decision-making and is developed in a two-tier fuzzy modeling process. First in direct approach, the expert knowledge generates a subjective model to describe the characteristics of the system using fuzzy linguistic variables. Later in system identification, the field data is used to develop an objective model, which is eventually used in conjunction with the subjective model to provide a more reliable knowledge base for the expert system. The inference mechanism uses fuzzy approximate reasoning methods to process the encoded information of the knowledge base. |
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| Publication date | 2006-01-01 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | NRCC 46912
NRC-IRC-16114 |
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| NPARC number | 20377323 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 7841448b-1fdb-4635-9651-0fd7a4ca5d7c |
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| Record created | 2012-07-24 |
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| Record modified | 2020-04-22 |
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