Abstract
Purpose
Owing to the consumption of considerable resources in developing physical pipe prediction models and the fact that the statistical models cannot fit the failure records perfectly, the purpose of this paper is to use data mining method to analyze and predict the risks of water pipe failure via considering attributes and location of pipes in historical failure records. One of the Automatized Machine Learning (AutoML) methods, tree-based pipeline optimization technique (TPOT) was used as the key data mining technique in this research.
Design/methodology/approach
By considering pipeline attributes, environmental factors and historical pipeline broke/breaks records, a water pipeline failure prediction method is proposed in this research. Regression analysis, genetic algorithm, machine learning, data mining approaches are used to analyze and predict the probability of pipeline failure. TPOT was used as the key data mining technique. A case study was carried out in a specific area in China to investigate the relationships between pipeline broke/breaks and relevant parameters, such as pipeline age, materials, diameter, pipeline density and so on.
Findings
By integrating the prediction models for individual pipelines and small research regions, a prediction model is developed to describe the probability of water pipe failures and validated by real data. A high fitting degree is achieved, which means a good potential of using the proposed method in reality as a guideline for identifying areas with high risks and taking proactive measures and optimizing the resources allocation for water supply companies.
Originality/value
Different models are developed to have better prediction on regional or individual pipeline. A comparison between the predicted values with real records has shown that a preliminary model has a good potential in predicting the future failure risks.
Subject
Building and Construction,Architecture,Human Factors and Ergonomics
Reference35 articles.
1. A data mining approach to modelling of water supply assets;Urban Water Journal,2002
2. Development of pipe deterioration models for water distribution systems using EPR;Journal of Hydroinformatics,2008
3. A physical probabilistic model to predict failure rates in buried PVC pipelines;Reliability Engineering and System Safety,2007
4. Failure prediction and optimal scheduling of replacements in asbestos cement water pipes;Journal of Water Supply Research and Technology,2008
5. A strategy for optimal replacement of water pipes integrating structural and hydraulic indicators based on a statistical water pipe break model,2005
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献