Deep Learning-Enriched Stress Level Identification of Pretensioned Rods via Guided Wave Approaches

By introducing pre-compression/inverse moment through prestressing tendons or rods, prestressed concrete (PC) structures could overcome conventional concrete weakness in tension, and thus, these tendons or rods are widely accepted in a variety of large-scale, long-span structures. Unfortunately, prestressing tendons or rods embedded in concrete are vulnerable to degradation due to corrosion. These embedded members are mostly inaccessible for visual or direct destructive assessments, posing challenges in determining the prestressing level and any corrosion-induced damage. As such, ultrasonic guided waves, as one of the non-destructive examination methods, could provide a solution to monitor and assess the health state of embedded prestressing tendons or rods. The complexity of the guided wave propagation and scattering in nature, as well as high variances stemming from the structural uncertainty and noise interference PC structures may experience under complicated operational and harsh environmental conditions, often make traditional physics-based methods invalid. Alternatively, the emerging machine learning approaches have potential for processing the guided wave signals with better capability of decoding structural uncertainty and noise. Therefore, this study aimed to tackle stress level prediction and the rod embedded conditions of prestressed rods in PC structures through guided waves. A deep learning approach, convolutional neural network (CNN), was used to process the guided wave dataset. CNN-based prestress level prediction and embedding condition identification of rods were established by the ultrasonic guided wave technique. A total of fifteen scenarios were designed to address the effectiveness of the stress level prediction under different noise levels and grout materials. The results demonstrate that the deep learning approaches exhibited high accuracy for prestressing level prediction under structural uncertainty due to the varying surrounding grout materials. With different grout materials, accuracy could reach up to 100% under the noise level of 90 dB, and still maintain the acceptable range of 75% when the noise level was as high as 70 dB. Moreover, the t-distributed stochastic neighbor embedding technology was utilized to visualize the feature maps obtained by the CNN and illustrated the correlation among different categories. The results also revealed that the proposed CNN model exhibited robustness with high accuracy for processing the data even under high noise interference.

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