Water loss in distribution networks is a critical challenge for civil engineers, especially with aging infrastructure, rapid urbanization, and increasing water scarcity. ASCE's 2025 Report Card for America’s Infrastructure reports that 126 billion cubic meters of water (or approximately 33.3 trillion gallons) are lost annually due to aging infrastructure. As a primary contributor to this loss, leaks often remaining undetected for long periods. Researchers Pranav Agrawal and Sriram Narasimhan explore a leak localization process that goes beyond detection to pinpoint the exact location of leaks.
By leveraging hydrophones and analyzing low-frequency acoustic signals, their study “Leak Localization in Operational Water Distribution Networks Using a Cross Correlation–Based Approach” explores conditions under which leak locations can be theoretically and practically identified in real-world operational networks. The proposed cross-correlation technique offers a practical, sensor-based solution that can operate over large distances and complex network geometries. This means faster, more cost-effective leak localization without the need for exhaustive calibration or synthetic data generation. Implementing an acoustic-based strategy can help streamline maintenance, minimize excavation costs, and enhance the sustainability of water distribution systems. Read about their findings in the Journal of Pipeline Systems Engineering and Practice at https://ascelibrary.org/doi/10.1061/JPSEA2.PSENG-1866. The abstract is below.
Abstract
Water distribution networks (WDNs) buried underground constitute a significant part of the urban infrastructure. Leaks in WDNs cause substantial water loss; hence it is critical to quickly detect, localize, and fix leaks. Acoustic sensors combined with cross-correlation signal processing methods are widely used to detect and locate leaks in WDNs; however, most studies have focused on simulated leaks in lab-scale test beds or in straight pipe segments where a line of sight exists between the leak and the measurement locations. This research presents leak localization results based on acoustic (hydrophone) data from a full-scale WDN in California, USA, that encompasses multiple bends and T-junctions. This system presents practical challenges in implementing conventional correlation techniques due to non-line-of-sight conditions. The problem of localizing a leak in this situation is solved using the cross-correlation-based maximum likelihood estimation method, which addresses non-line-of-sight and multipath challenges in this application. Additionally, an optimization-based framework is proposed to correct errors that occur in the time delay of arrivals resulting from desynchronized sensor clocks. The proposed method shows promising results with predicting a localization estimate with an approximate error in the range of 0.6%–3.06% of the total pipeline length surveyed.
Learn how this acoustic method can enhance the ability to detect water line leaks in the ASCE Library: https://ascelibrary.org/doi/10.1061/JPSEA2.PSENG-1866.
