@Article{sdhm.2025.066558,
AUTHOR = {Yuhao Zhang, Peiqiang Zhao, Xing Chen, Shaoxuan Zhang, Xinglin Zhang},
TITLE = {Dual-Stream Deep Learning for Health Monitoring of HDPE Geomembranes in Landfill Containment Systems},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {19},
YEAR = {2025},
NUMBER = {5},
PAGES = {1343--1365},
URL = {http://www.techscience.com/sdhm/v19n5/63669},
ISSN = {1930-2991},
ABSTRACT = {The structural integrity monitoring of high-density polyethylene (HDPE) geomembranes in landfill containment systems presents a critical engineering challenge due to the material’s vulnerability to mechanical degradation and the complex vibration propagation characteristics in large-scale installations. This study proposes a dual-stream deep learning framework that synergistically integrates raw vibration signal analysis with physics-guided feature extraction to achieve precise rupture detection and localization. The methodology employs a hierarchical neural architecture comprising two parallel branches: a 1D convolutional network processing raw accelerometer signals to capture multi-scale temporal patterns, and a physics-informed branch extracting material-specific resonance features through continuous wavelet transform (CWT) and energy ratio quantification. A novel gated attention mechanism dynamically fuses these heterogeneous modalities, adaptively weighting their contributions based on localized signal characteristics—prioritizing high-frequency transient features near damage zones while emphasizing physics-derived energy anomalies in intact regions. Spatial correlations among distributed sensors are modeled via graph convolutional networks (GCNs) that incorporate geometric topology and vibration transmission dynamics, enabling robust anomaly propagation analysis.},
DOI = {10.32604/sdhm.2025.066558}
}