35 citations as recorded by crossref.

1. A two-time-scale point process model of water main breaks for infrastructure asset management P. Lin & X. Yuan https://doi.org/10.1016/j.watres.2018.11.066
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3. Conceptual Water Main Failure Risk: Self-Excitation, Pipe Age, and Statistical Modeling Performance C. Hammond et al. https://doi.org/10.1061/JWRMD5.WRENG-6432
4. An Approach to Predict the Failure of Water Mains Under Climatic Variations Z. Almheiri et al. https://doi.org/10.1007/s40891-020-00237-8
5. Integrated approach for pipe failure prediction and condition scoring in water infrastructure systems T. Rifaai et al. https://doi.org/10.1016/j.ress.2021.108271
6. Comparative Review of Water Main Failure Prediction Models: Physical and Data-Driven Approaches M. Khashei et al. https://doi.org/10.1061/JWRMD5.WRENG-6866
7. Predicting the Timing of Water Main Failure Using Artificial Neural Networks R. Harvey et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000354
8. Review of statistical water main break prediction models M. Nishiyama & Y. Filion https://doi.org/10.1139/cjce-2012-0424
9. An Optimal Maintenance and Replacement Strategy for Deteriorating Water Mains P. Lin et al. https://doi.org/10.3390/w14132097
10. A novel ‘pressure index’ for predicting number of pipe bursts in water distribution system S. Akbarkhiavi & M. Imteaz https://doi.org/10.1680/jwama.20.00076
11. An evolution of statistical pipe failure models for drinking water networks: a targeted review N. Barton et al. https://doi.org/10.2166/ws.2022.019
12. Scheduling Renewal of Water Pipes While Considering Adjacency of Infrastructure Works and Economies of Scale A. Nafi & Y. Kleiner https://doi.org/10.1061/(ASCE)WR.1943-5452.0000062
13. Analysis of risk management methods used in trenchless renewal decision making G. Vladeanu & J. Matthews https://doi.org/10.1016/j.tust.2017.11.025
14. Extending the Yule process to model recurrent pipe failures in water supply networks Y. Le Gat https://doi.org/10.1080/1573062X.2013.783088
15. Prioritizing Water Mains for Inspection and Maintenance Considering System Reliability and Risk H. Phan et al. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000324
16. Role of Cathodic Protection on Physical Condition and Pipe Break Linkage T. Dodanwala et al. https://doi.org/10.1061/JPSEA2.PSENG-1596
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18. Performance Estimation of a Remote Field Eddy Current Method for the Inspection of Water Distribution Pipes S. Duchesne et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000136
19. Minimising the total cost of renewal and risk of water infrastructure assets by grouping renewal interventions M. Rokstad & R. Ugarelli https://doi.org/10.1016/j.ress.2015.05.014
20. A data-driven framework for failure probability prediction of water supply pipelines: Integrating feature selection and homogeneous grouping C. Zong et al. https://doi.org/10.1016/j.ress.2026.112623
21. Probability of network disconnection of water distribution system for maintenance prioritization H. Phan et al. https://doi.org/10.2166/aqua.2018.097
22. Analysis and Modeling of Pressure Pipe Failures in Auckland, New Zealand L. Lopez et al. https://doi.org/10.1061/JWRMD5.WRENG-6242
23. Review on Statistical Based Methods of Measuring the Water Pipes Reliability A. Bubtiena et al. https://doi.org/10.4028/www.scientific.net/AMR.230-232.1327
24. Comparison of Pipeline Failure Prediction Models for Water Distribution Networks with Uncertain and Limited Data L. Jenkins et al. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000181
25. Comparison of Statistical Deterioration Models for Water Distribution Networks H. Osman & K. Bainbridge https://doi.org/10.1061/(ASCE)CF.1943-5509.0000157
26. Analysis of Wastewater and Water System Renewal Decision-Making Tools and Approaches J. Matthews et al. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000114
27. Risk Assessment Methods for Urban Water Distribution Networks: A State-of-the-Art Review of Indicator, Statistical, and Machine Learning Approaches G. Chen et al. https://doi.org/10.3390/app16073443
28. A review of climatic impacts on water main deterioration T. Ahmad et al. https://doi.org/10.1016/j.uclim.2023.101552
29. Modeling the Frequency of Water Main Breaks in Water Distribution Systems: Random-Parameters Negative-Binomial Approach H. Zamenian et al. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000336
30. Research on pipe burst in water distribution systems: knowledge structure and emerging trends C. Wang et al. https://doi.org/10.2166/aqua.2022.150
31. Efficacy of Tree-Based Models for Pipe Failure Prediction and Condition Assessment: A Comprehensive Review M. Latifi et al. https://doi.org/10.1061/JWRMD5.WRENG-6334
32. A comparison between multivariate adaptive regression splines and regressive convolution neural network with support vector regression for pipe burst rate prediction on limited dataset A. Ravanbakhsh et al. https://doi.org/10.1080/1573062X.2022.2105238
33. Bayesian Belief Networks for predicting drinking water distribution system pipe breaks R. Francis et al. https://doi.org/10.1016/j.ress.2014.04.024
34. A comprehensive criteria-based multi-attribute decision-making model for rehabilitation of water distribution systems S. Salehi et al. https://doi.org/10.1080/15732479.2017.1359633
35. Statistical failure models for water distribution pipes – A review from a unified perspective A. Scheidegger et al. https://doi.org/10.1016/j.watres.2015.06.027