Study on evaluation model of rate grade of soil corrosion for pipeline

Zhao, Zhifeng; Wu, Dan; Gao, Guanbin; Fan, Honghai

doi:10.1109/icmsp53480.2021.9513220

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…(c) Pruning of decision tree: the repeated classification and opposite judgment are deleted to improve the fault tolerance and adaptability of hierarchical evaluation. (d) Selecting the result attribute: the corrosion grade is used as the leaf node of decision tree classification, and the evaluation model of decision tree is established [13].…”

Section: Decision Tree Analysis Methods (Knowledge Decision)mentioning

confidence: 99%

Data Analysis and Knowledge Mining of Machine Learning in Soil Corrosion Factors of the Pipeline Safety

Zhao

Chen

Fan

et al. 2022

Computational Intelligence and Neuroscience

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The purpose of this research is to enhance the ability of data analysis and knowledge mining in soil corrosion factors of the pipeline. According to its multifactor characteristics, the rough set algorithm is directly used to analyze and process the observation data without considering any prior information. We apply rough set algorithm to delete the duplicate same information and redundant items and simplify the condition attributes and decision indicators from the decision table. Combined with the simplified index, the decision tree method is used to analyze the root node and branch node of it, and the knowledge decision model is constructed. With the Python machine learning language and PyCharm Community Edition software, the algorithm functions of rough set and decision tree are realized, so as to carry out artificial intelligence analysis and judgment of the soil corrosion factor data in pipeline. Taking the area of loam soil corrosion as an example, the data analysis and knowledge mining of its multifactors original data are carried out through the model. The example verifies that the evaluation and classification rules of the model meet the requirements, and there are no problems such as inconsistency and heterogeneity. It provides decision-making service and theoretical basis for the soil corrosion management of pipeline.

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Section: Decision Tree Analysis Methods (Knowledge Decision)mentioning

confidence: 99%

Data Analysis and Knowledge Mining of Machine Learning in Soil Corrosion Factors of the Pipeline Safety

Zhao

Chen

Fan

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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“…Given the corrosion status of the pipeline anti-corrosive coating, the future corrosion status is only related to the corrosion status at the time of detection, which has the characteristics of the Markov chain. Therefore, the corrosion degradation process can be viewed as a Markov chain [ 16 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning in the Reliability Evaluation of Pipelines for the External Anticorrosion Coating

Zhao

Chen

Fan

et al. 2022

Computational Intelligence and Neuroscience

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The purpose of this research is to enhance the analysis of the reliability status for external anticorrosive coatings. With the limitation and insufficiency of the static evaluation method, we study and construct an evaluation method of dynamic reliability for the anticorrosive layer, integrating the trend analysis of the Markov chain and the set pair theory. This method is implemented by the machine learning software of PyCharm community edition, based on Python language. The algorithm utilizes the connection degree in the set pair theory to determine the risk levels of the anticorrosive coating systems. According to the characteristics of the dynamic change of the anticorrosive layer with time, we built the mathematical evaluation model by combining it with the nonaftereffect property of the Markov chain. Therefore, we can make a dynamic and useful analysis for the reliability grade of the anticorrosive coating and assess the effectiveness grade of the changed reliability for the anticorrosive coating after some time. This method can effectively evaluate the reliability level of the anticorrosion coating through the example of big data of detection points. Under national standards, we provide the theoretical basis for pipeline maintenance within detection cycle requirements.

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Corrosion Experimental Investigation on Pipeline Connecting Pieces under Controlled Conditions

Petrescu

Chirilă

Deaconu

et al. 2023

2023 13th International Symposium on Advanced Topics in Electrical Engineering (ATEE)

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Study on evaluation model of rate grade of soil corrosion for pipeline

Cited by 3 publications

References 3 publications

Data Analysis and Knowledge Mining of Machine Learning in Soil Corrosion Factors of the Pipeline Safety

Data Analysis and Knowledge Mining of Machine Learning in Soil Corrosion Factors of the Pipeline Safety

Application of Machine Learning in the Reliability Evaluation of Pipelines for the External Anticorrosion Coating

Corrosion Experimental Investigation on Pipeline Connecting Pieces under Controlled Conditions

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