Intelligent Perception Method for Delamination of Cement Emulsified Asphalt Mortar in Slab Tracks on Bridges Using Simulated Acoustic Radiation Signals

Abstract:

This paper proposes an intelligent perception method for detecting delamination of cement emulsified asphalt （CA） mortar in slab tracks based on the structure-borne sound signals collected by trackside acoustic sensors. A vehicle-track-bridge coupled vibration calculation model and an acoustic-vibration coupling analysis model are established to simulate the vibration and acoustic radiation response of the slab tracks and bridge structures under the dynamic loads caused by passing trains. The influence of CA mortar delamination on the vibration and acoustic radiation response is analyzed. By using simulated acoustic data and support vector machines （SVM）， binary and multi-class classification recognition of 15 types of CA mortar delamination are implemented. The results show that compared with displacement response， acceleration response and acoustic radiation response are more sensitive to CA mortar delamination. The classification performance of the binary SVM model varies for different measurement points， but the accuracy can generally reach over 85 %. The classification accuracy of the binary SVM model trained based on the sound pressure data at a specific measurement point decreases by 10 % to 30 % for unknown measurement points compared with that for the specific measurement points. The fusion of multi-point position data can improve the accuracy of multi-class classification recognition.