Combining recorded failures and expert opinion in the development of ANN pipe failure prediction models

ABSTRACT

Buried pipes comprise a significant portion of assets of a water utility. With time, these pipes inevitably fail. Failure prediction enables infrastructure managers to estimate long-term failure trends for budgetary planning purposes and identify critical pipes for preventive intervention planning. For short-term prioritization, machine learning based algorithms appear to have superior predictive performance compared to traditional survival analysis based models. These models are typically stratified by material resulting in the exclusion of newer pipe materials such as polyethylene and corrosion-protected ductile iron, despite their prevalence in modern networks. In this paper, an application of an existing methodology is presented to estimate time to next failure using artificial neural networks (ANNs). The novelties of the approach are 1) including material as an input parameter instead of training several material-specialized models and, 2) addressing right-censored data by combining soft and hard deterioration data. The model is intended for use in short-term prioritization.

KEYWORDS:

• Artificial neural networks
• failure prediction
• water distribution networks

Acknowledgments

The authors most gratefully acknowledge the water utility of Geneva (SIG) for providing the data for this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Sean Kerwin

Sean Kerwin is a scientific assistant in the Infrastructure Management Group at the Institute of Construction and Infrastructure Management (IBI) of the ETH Zurich. His research focuses on intervention planning and asset management of water distribution networks.

Borja Garcia de Soto

Borja García de Soto is an Assistant Professor of Civil and Urban Engineering at New York University Abu Dhabi (NYUAD).

Bryan Adey

Prof. Dr. Bryan Adey is the Professor for Infrastructure Management at the Swiss Federal Institute of Technology in Zürich (ETHZ), Switzerland.

Kleio Sampatakaki

Kleio Sampatakaki and Hannes Heller are graduates of the Master's degree program in Civil Engineering at ETHZ. Ms. Sampatakaki is working as a structural engineer at the Gruner Wepf AG Zürich. Mr. Heller is working as a civil engineer at EBP.