Modelling of Location Uncertainties of Leakages in Pressurized Buried Water Mains Using Leak Noise Correlator (LNC)

This paper investigates the specific positioning accuracies and uncertainties associated with the measurement of acoustic leakage noise correlation (LNC) in underground pressurized water mains, treating them as acoustic waveguides. It begins by identifying three key intrinsic sources of measurement errors: (1) the speed of acoustic waves in the water mains as influenced by pipe material, wall thickness, modulus of elasticity, and bulk modulus; (2) the distance between the two accelerometers used for correlation; (3) the time delay from the point of leakage to the accelerometers. A mathematical uncertainty model was developed to compute sensitivity coefficients, enabling the propagation of measurement errors from these sources. This was validated through seven sets of full-scale experiments conducted at Q-Leak, a 25,000 sq. ft. test site in Hong Kong. This study ultimately quantified and assessed the contributions of individual error sources to the overall uncertainty, allowing for the prioritization of factors that have the most significant impact in various scenarios. The findings reveal that Young’s modulus and pipe wall thickness are the primary factors affecting measurements for both plastic and metal pipes. Additionally, a universal in-house program, “LNC uncertainty calculator,” was developed to provide insights into the buffer ranges for confirming suspected leak locations while considering constraints within the uncertainty budget. This research highlights the critical but often overlooked area of uncertainty modeling in leak detection for pressurized buried water mains, offering valuable insights intended to enhance operational strategies and maintenance practices within the industry. This research provides a robust framework for understanding the accuracy of leak detection. This means operators can better interpret the reliability of their measurements, leading to consistent decision-making across different situations and minimizing the risk of misidentifying the presence or absence of leakage. In addition, the insights gained from prioritizing factors that affect measurement accuracy allow engineers and operators to make informed decisions about where to focus their resources and efforts. This can lead to more effective maintenance strategies that are tailored to specific conditions, thereby optimizing operational efficiency.