Intelligent Monitoring of Shear Damage Evolution at Bonded Sandstone Interfaces Based on ViT and Piezoelectric Ultrasonic Testing.

This paper presents a method for monitoring damage evolution at sandstone-binding material interfaces by combining a Vision Transformer (ViT) deep learning model with piezoelectric ultrasonic monitoring. Direct shear tests were conducted on bonded weak sandstone specimens. The results indicate that the damage evolution process can be divided into four stages: initial elastic, compaction and stabilization, crack propagation and coalescence, and frictional sliding and interlocking. Ultrasonic signals acquired during loading reveal that interface damage evolution is in good agreement with the time-domain waveforms, Continuous Wavelet Transform (CWT) time-frequency spectra, and wavelet packet energy. Based on the ViT-Small/16 backbone, a ViT model with adaptive frequency-feature extraction was developed for small-sample and cross-specimen interfacial damage-stage identification, using the Stage I health observations of each specimen prior to loading as the reference. Results from five repeated runs with different random seeds show that the method achieved an accuracy of 93.23% ± 0.72% and an F1-score of 92.32% ± 0.90%, demonstrating favorable recognition performance and stability under the current condition. This study offers insights into the damage monitoring and subsequent warning of similar binary interfaces in tunnel engineering, geotechnical engineering, and stone cultural heritage conservation.