Noninvasive identification of lead water service lines using hammer-induced stress waves
Year: 2026
Type: article
Abstract: Identifying lead water service lines (LSLs) without excavation remains a significant challenge for utilities across the United States, as existing noninvasive methods are limited in their practical use and scalability. This study introduces a stress wave-based technique for detecting LSLs. This method is applicable to both the utility and customer-owned sides of service lines (SLs) without requiring access into homes, making it suitable for large-scale field deployment. Stress waves are generated by striking an extension rod at the curb-stop valve, while surface-mounted accelerometers capture the resulting signals. The method was field-tested on more than 300 SLs across 17 U.S. cities. All recorded signals were processed using wavelet transform techniques to extract robust time–frequency features. These features were then used as inputs to three deep learning models: 2D-convolutional neural network (CNN), CNN-BiLSTM, and a TimesFormer-based transformer. Each model was trained to perform binary classification between lead and non-lead SL materials. Among the models evaluated, the TimeSformer demonstrated the highest performance, achieving an accuracy of 80% on a held-out test set and 82% in a blind-tested set of 66 SLs from new locations. These results suggest that the stress wave technology proposed in this study, combined with wavelet-based signal processing and deep learning models offers a promising noninvasive solution for identifying LSLs under diverse field conditions.
Source: Smart Materials and Structures
Institution: Drexel University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
OpenAlex ID: W7164507791