Title: Development of models for prediction of water distribution pipeline failures for isolated settlement using artificial neural networks

Authors: Paul Amaechi Ozor; Solomon Onyekachukwu Onyedeke; Charles Mbohwa

Addresses: Faculty of Engineering and the Built Environment, Department of Quality and Operations management, University of Johannesburg, DoornFontein Campus, 2092, South Africa; Faculty of Engineering, Department of Mechanical Engineering, University of Nigeria Nsukka, Enugu State, 410001, Nigeria ' Faculty of Engineering, Department of Mechanical Engineering, University of Nigeria Nsukka, Enugu State, 410001, Nigeria ' Faculty of Engineering and the Built Environment, Department of Quality and Operations Management, University of Johannesburg, DoornFontein Campus, 2092, South Africa

Abstract: The development of artificial neural network (ANN) models for the prediction of failure of water pipelines in rural communities is undertaken. The research use analytical procedures. This approach was illustrated using a 10-year primary pipelines failure data taken from a typical isolated settlement. Four ANN models were established; three models based on specific material type and a combinatorial model. Validation of the models on unseen data gave impressive results, on the basis of the performance indices. The sensitivity analysis shows that the pipe thickness impacts the model output most, whereas the installation year had the least relative importance in predicting pipeline failure. The models predicted failure parameters appreciably, and determined the benefit index, which allows the elaboration of a strategy for achieving realistic maintenance policies. The study shows that the ANN supported with EPANET software can provide a good means of analysing pipeline failures and optimising the maintenance function.

Keywords: maintenance policies; water pipeline failure; artificial neural network; sensitivity analysis; benefit index.

DOI: 10.1504/IJTPM.2022.126140

International Journal of Technology, Policy and Management, 2022 Vol.22 No.4, pp.348 - 368

Received: 24 Nov 2020
Accepted: 02 May 2021
Published online: 13 Oct 2022 *

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