Identifying Lead Water Service Lines Using Ultrasonic Stress Wave Propagation and 1D-Convolutional Neural Network

Abstract Water utilities across the United States face challenges in identifying lead water service lines without excavation, as existing non-destructive methods have notable limitations. This study introduces a non-invasive technology based on stress wave propagation to detect service line materials on both the public (utility) and private (customer) sides. Stress waves are generated at the curb-stop valve of the service line by striking an extension rod with an instrumented hammer, which records the input impact signal. Piezoelectric accelerometer sensors placed on the soil surface then detect the pipe’s responses (output signals). This technology was field-tested in 419 service lines across 20 cities of the US. The collected data underwent several signal processing steps for the calculation of the frequency response function (FRF). Since the data was collected from various cities and locations, there were significant variations in soil depth, soil properties, and surface conditions. These variations made it challenging to develop a physics-based algorithm that accurately differentiates lead from non-lead materials (such as copper, galvanized steel, and plastic). A 1D-Convolutional Neural Network (1D-CNN) was developed that uses combined real and imaginary FRF components as input to classify lead versus non-lead materials. The model was trained on 80% of the service line FRF data, with 10% used for validation and the remaining 10% for testing. To evaluate the model’s performance, a confusion matrix was employed to calculate accuracy, precision, recall, and F1 score using the testing data. The model achieved 80% accuracy on test data and 80.5% accuracy on 41 blind-tested service lines. These results indicate that the stress wave technology proposed in this study, combined with signal processing and 1D-CNN model, offers a promising solution for non-invasively identifying lead service lines in diverse field conditions.