摘要
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.Themethodology 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 aremodeled via graph convolutional networks(GCNs)that incorporate geometric topology and vibration transmission dynamics,enabling robust anomaly propagation analysis.
基金
supported by the Research and Talent Development Base for Intelligent Monitoring,Early Warning,and Emergency Management of Major Environmental Risk Sources in the Yellow River Basin(24RCXM58)
Research and Demonstration of Key Technologies for Long-Term Service and Smart Operation of Major Environmental Safety Infrastructure(Solid and Hazardous Wastes)in the Yellow River Basin(24ZYQA025).
