Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm
Pipeline corrosion prediction (PCP) is an important technology for pipeline maintenance and management. How to accurately predict pipeline corrosion is a challenging task. To address the drawback of the poor prediction accuracy of the grey model (GM(1,1)), this paper proposes a method named ETGM(1,1...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/11/6/289 |
| _version_ | 1827662421577170944 |
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| author | Shiguo Li Hualong Du Qiuyu Cui Pengfei Liu Xin Ma He Wang |
| author_facet | Shiguo Li Hualong Du Qiuyu Cui Pengfei Liu Xin Ma He Wang |
| author_sort | Shiguo Li |
| collection | DOAJ |
| description | Pipeline corrosion prediction (PCP) is an important technology for pipeline maintenance and management. How to accurately predict pipeline corrosion is a challenging task. To address the drawback of the poor prediction accuracy of the grey model (GM(1,1)), this paper proposes a method named ETGM(1,1)-RABC. The proposed method consists of two parts. First, the exponentially transformed grey model (ETGM(1,1)) is an improvement of the GM(1,1), in which exponential transformation (ET) is used to preprocess the raw data. Next, dynamic coefficients, instead of background fixed coefficients, are optimized by the reformative artificial bee colony (RABC) algorithm, which is a variation of the artificial bee colony (ABC) algorithm. Experiments are performed on actual pipe corrosion data, and four different methods are included in the comparative study, including GM(1,1), ETGM(1,1), and three ETGM(1,1)-ABC variants. The results show that the proposed method proves to be superior for the PCP in terms of Taylor diagram and absolute error. |
| first_indexed | 2024-03-10T00:24:52Z |
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| id | doaj.art-8976f16e8f6845c68b00777666b68fba |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-10T00:24:52Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-8976f16e8f6845c68b00777666b68fba2023-11-23T15:35:33ZengMDPI AGAxioms2075-16802022-06-0111628910.3390/axioms11060289Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony AlgorithmShiguo Li0Hualong Du1Qiuyu Cui2Pengfei Liu3Xin Ma4He Wang5School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, ChinaPipeline corrosion prediction (PCP) is an important technology for pipeline maintenance and management. How to accurately predict pipeline corrosion is a challenging task. To address the drawback of the poor prediction accuracy of the grey model (GM(1,1)), this paper proposes a method named ETGM(1,1)-RABC. The proposed method consists of two parts. First, the exponentially transformed grey model (ETGM(1,1)) is an improvement of the GM(1,1), in which exponential transformation (ET) is used to preprocess the raw data. Next, dynamic coefficients, instead of background fixed coefficients, are optimized by the reformative artificial bee colony (RABC) algorithm, which is a variation of the artificial bee colony (ABC) algorithm. Experiments are performed on actual pipe corrosion data, and four different methods are included in the comparative study, including GM(1,1), ETGM(1,1), and three ETGM(1,1)-ABC variants. The results show that the proposed method proves to be superior for the PCP in terms of Taylor diagram and absolute error.https://www.mdpi.com/2075-1680/11/6/289GM(1,1)artificial bee colony algorithmpipeline corrosion predictionparameter optimization |
| spellingShingle | Shiguo Li Hualong Du Qiuyu Cui Pengfei Liu Xin Ma He Wang Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm Axioms GM(1,1) artificial bee colony algorithm pipeline corrosion prediction parameter optimization |
| title | Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm |
| title_full | Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm |
| title_fullStr | Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm |
| title_full_unstemmed | Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm |
| title_short | Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm |
| title_sort | pipeline corrosion prediction using the grey model and artificial bee colony algorithm |
| topic | GM(1,1) artificial bee colony algorithm pipeline corrosion prediction parameter optimization |
| url | https://www.mdpi.com/2075-1680/11/6/289 |
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