Pipeline condition assessment: failure risk factors, noninvasive methods, and some water utility perspectives

ABSTRACT Water utilities must assess the condition of buried pipelines without excavation or interruption of service. We review 20 pipeline risk models – specifically for the likelihood of failure – and 10 leak detection methods. We find that common risk factors include physical attributes (e.g., pipe age, material, diameter), operational attributes (e.g., pressure, flow, repair history), and environmental attributes (e.g., soil type, bury depth, fault proximity). The models contain 4–27 risk factors, differing in inclusion and weight. For an industry perspective, we interview water utilities in Utah, USA, on their condition assessment and leak detection practices. Physical attributes are most available, and environmental attributes are least available. Many models are unworkable because the water utilities lack the required data. Instead, they use acoustic methods, if any, for reactive leak detection. We recommend developing data-light risk models (using a few common attributes) to inform the best leak detection method for a given water system.

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Publication Details

Journal
Water Practice & Technology
Published
2026-03-13
DOI
https://doi.org/10.2166/wpt.2026.246
Primary Topic
Water Systems and Optimization
Type
article
Field-Weighted Citation Impact
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article

Pipeline condition assessment: failure risk factors, noninvasive methods, and some water utility perspectives

Water Practice & Technology
Water Systems and Optimization
article

Pipeline condition assessment: failure risk factors, noninvasive methods, and some water utility perspectives

article en

Abstract

ABSTRACT Water utilities must assess the condition of buried pipelines without excavation or interruption of service. We review 20 pipeline risk models – specifically for the likelihood of failure – and 10 leak detection methods. We find that common risk factors include physical attributes (e.g., pipe age, material, diameter), operational attributes (e.g., pressure, flow, repair history), and environmental attributes (e.g., soil type, bury depth, fault proximity). The models contain 4–27 risk factors, differing in inclusion and weight. For an industry perspective, we interview water utilities in Utah, USA, on their condition assessment and leak detection practices. Physical attributes are most available, and environmental attributes are least available. Many models are unworkable because the water utilities lack the required data. Instead, they use acoustic methods, if any, for reactive leak detection. We recommend developing data-light risk models (using a few common attributes) to inform the best leak detection method for a given water system.

Water Practice & Technology
Brigham Young University (US)
Clean water and sanitation
Openalex Percentile: Top 10%
Water Systems and Optimization
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