Browsing by Author "Barton, Neal Andrew"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Predicting the risk of pipe failure using gradient boosted decision trees and weighted risk analysis
    (Nature Publishing Group, 2022-06-17) Barton, Neal Andrew; Hallett, Stephen H.; Jude, Simon R.; Tran, Trung Hieu
    Pipe failure prediction models are essential for informing proactive management decisions. This study aims to establish a reliable prediction model returning the probability of pipe failure using a gradient boosted tree model, and a specific segmentation and grouping of pipes on a 1 km grid that associates localised characteristics. The model is applied to an extensive UK network with approximately 40,000 km of pipeline and a 14-year failure history. The model was evaluated using the Receiver Operator Curve and Area Under the Curve (0.89), briers score (0.007) and Mathews Correlation Coefficient (0.27) for accuracy, indicating acceptable predictions. A weighted risk analysis is used to identify the consequence of a pipe failure and provide a graphical representation of high-risk pipes for decision makers. The weighted risk analysis provided an important step to understanding the consequences of the predicted failure. The model can be used directly in strategic planning, which sets long-term key decisions regarding maintenance and potential replacement of pipes.
  • Thumbnail Image
    ItemOpen Access
    Proactively managing drinking water distribution networks: A data- driven, statistical modelling approach to predict the risk of pipe failure.
    (Cranfield University, 2022-02) Barton, Neal Andrew; Hallett, Stephen; Jude, Simon
    Water distribution networks are critical infrastructures, providing clean water to millions of people. 3 billion litres of water are lost through pipe failure every day in the UK, impacting serviceability. Statistical pipe failure models can reduce pipe failures by providing valuable insights to enhance decision-making and promote proactive management. This research aims to understand the complexity of pipe failures in water distribution networks and develop a methodology for a reliable pipe failure model that identifies the risk of failure. Through an embedded case study and data-driven approach, several Objectives have been undertaken that comprise the body of research delivered through several research papers. This study offers several contributions to the immediate field of pipe failure research. Firstly, the findings investigate new factors that form the various modes and mechanisms of pipe failure, using alternative methods not commonly used in pipe failure research are used, including Generalized Additive Model and Dijkstra’s algorithm, and using data from a large UK water distribution network. Secondly, the research develops a suitable methodology for predicting annual pipe failures using an advanced machine learning method; a methodology that is easily transferrable. Thirdly, the research provides a useful means of predicting the risk of failure and visualising the results. Fourthly, the research investigates the challenges of pipe failure models using a semi-structured interview approach to review current practice. Finally, the research contributes by exploring several different data-driven methods and an embedded case study design to contribute to the broader context of pipe failure modelling. The approach presented in this research provides a methodological framework to enhance decision-making for asset management of pipes in clean water networks. Furthermore, it highlights the main limitations, particularly data quality and quantity, data-pre-processing, and model development, highlighting areas for future progress.