Automated Impact Echo Spectrum Anomaly Detection Using U-Net Autoencoder

Abstract

Condition assessments are crucial in the field of civil engineering to avoid potential safety hazards and expensive repairs. For this, non-destructive testing methods like the Impact Echo method are used to detect defects like honeycombs inside of concrete without causing additional damage. This study introduces a four-step method and developed the U-Net Autoencoder (U-AE) to identify such defects from Impact Echo measurements. Our process involves preparing and learning from both simulated and real-world data to ensure an accurate anomaly detection. The key findings of this work demonstrate crucial aspects of data preparation and a significant performance enhancement achieved through pretraining with simulated data followed by fine-tuning with real-world datasets. Our approach effectively counteracts the information loss typically caused by dimension reduction, discovering an optimal balance in the latent space. Overall, this research introduces a novel architecture for detecting defects in concrete structures, marking a new advancement the practice of maintaining and inspecting civil infrastructure.