CNN-SVM enhanced impact acoustics for unbonded length detection in grouted rock bolts

This study addresses limitations in the non‑destructive evaluation of grouted rock bolts, notably their susceptibility to ambient noise interference and reliance on manual feature extraction by experts. An intelligent framework integrating impact acoustics and deep learning is proposed. A theoretical model, derived from dynamic equation analysis and experimental fitting, establishes a strong negative correlation between the unbonded length and the fundamental frequency. Experiments were conducted on three‑layer rock bolt models with five distinct bonding conditions, where impulse excitation was applied to acquire acoustic signals. Wavelet transform was employed to convert one‑dimensional time‑domain signals into two‑dimensional time–frequency representations, thereby enriching feature representation for model input. Seven classification algorithms were evaluated; the hybrid CNN–SVM model attained optimal classification accuracy (100%) under controlled laboratory conditions, significantly outperforming other methods and establishing a performance benchmark for ideal scenarios. Although real‑world deployment may be affected by environmental variability, the proposed approach exhibits robust performance within the experimental scope. By integrating theoretical modeling, wavelet‑based signal dimension enhancement, and deep learning, this method provides an automated, high‑precision, and interpreter‑independent solution for detecting grouting defects, demonstrating potential for industrial quality assurance in rock bolt systems.