Characterization and Application of Acoustic Signal Attenuation in Water Supply Pipelines

<p dir="ltr">The identification and localization of pipeline leakages using acoustic methods are crucial for the effective management of water distribution systems (WDSs) . A primary challenge in acoustic leakage detection is signal attenuation as it propagates through pipelines, ultimately dissipating into background noise. This study categorizes the contributing factors of attenuation into distributed acoustic loss and local acoustic loss. For distributed acoustic loss, a mathematical model was developed to characterize its relationship with key influencing factors. Experimental data collected from steel pipes were utilized to validate the model. The findings indicate that in the low-frequency range, the model’s attenuation coefficient exhibits a linear correlation with frequency. Local acoustic loss, in contrast, is induced by pipeline fittings and was demonstrated to function as a low-pass filter. Specifically, signals below 800 Hz exhibit significantly lower acoustic loss coefficients compared to higher-frequency signals. The dissipation of acoustic signals further implies the existence of a propagation distance boundary. Given that the acoustic signal does not propagate at a fixed frequency or with uniform intensity, estimating this boundary using a probabilistic distribution method is more appropriate. The results suggest that for steel pipelines, the spacing between vibration sensors should be less than 100 meters to achieve 90% coverage of leakage events. Overall, this study elucidates the attenuation characteristics of acoustic signals within pipelines and provides insights into the optimal positioning of sensors. These findings offer valuable guidance for future research in pipeline acoustic detection and benefit water utilities in enhancing leakage localization strategies.</p><p dir="ltr">This paper was presented at the 21st Computing and Control in the Water Industry Conference (CCWI 2025) at the University of Sheffield (1st - 3rd September 2025).</p>