Figure - available from: Journal of Civil Structural Health Monitoring
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GPR forward simulation for extending samples. a Three-dimensional schematic diagram of a random soil model; b GPR forward simulation images of an ideal soil model; c GPR forward simulation images of a random soil model; d GPR images obtained from on-site tests
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In recent years, urban road collapse incidents have been occurring with increasing frequency, particularly in populous cities. To mitigate road collapses, geophysical prospecting plays a crucial role in urban road inspections. Ground Penetrating Radar (GPR), a non-destructive technology, is extensively employed for detecting urban road damage, with...
Citations
... While numerical simulation is cost-effective, simulated images show characteristic differences from real images [50]. On-site collection, though costly, results in smaller datasets typically not exceeding 1000 images [51][52][53][54][55]. Therefore, compared to existing research, our dataset provides a larger-scale data foundation for internal road void image enhancement and intelligent recognition. ...
Internal road voids can lead to decreased load-bearing capacity, which may result in sudden road collapse, posing threats to traffic safety. Three-dimensional ground-penetrating radar (3D GPR) detects internal road structures by transmitting high-frequency electromagnetic waves into the ground and receiving reflected waves. However, due to noise interference during detection, accurately identifying void areas based on GPR-collected images remains a significant challenge. Therefore, in order to more accurately detect and identify the void areas inside the road, this study proposes an intelligent recognition method for internal road voids based on 3D GPR. First, extensive data on internal road voids was collected using 3D GPR, and the GPR echo characteristics of void areas were analyzed. To address the issue of poor image quality in GPR images, a GPR image enhancement model integrating multi-frequency information was proposed by combining the Unet model, Multi-Head Cross Attention mechanism, and diffusion model. Finally, the intelligent recognition model and enhanced GPR images were used to achieve intelligent and accurate recognition of internal road voids, followed by engineering validation. The research results demonstrate that the proposed road internal void image enhancement model achieves significant improvements in both visual effects and quantitative evaluation metrics, while providing more effective void features for intelligent recognition models. This study offers technical support for precise decision making in road maintenance and ensuring safe road operations.
... Non-destructive testing (NDE) techniques are essential in identifying structural sub-surface defects that are often missed during conventional visual inspections [18]. One such technology is ground-penetrating radar (GPR), which has been extensively utilized to detect material properties, corrosion-induced delamination and steel reinforcement corrosion [19][20][21][22]. Morries et al. employed ML models to characterize the relationships between GPR attributes and mechanical properties of concrete with various mix proportions [23]. ...
Nowadays, bridges play a crucial role, especially with the significant increase in the number of vehicles being driven worldwide. Hence, it is crucial to safeguard these structures from damage. This study aims to achieve this objective by proposing a novel hybrid framework for automated delamination detection of bridge decks based on ground penetrating radar (GPR), a mature technique utilized to localize underground deterioration or damage of bridges. The proposed framework comprises synchrosqueezed wavelet transform (SSWT), convolutional neural network (CNN), transfer learning, and metaheuristic optimization. First, original 1-D GPR signals undergo processing by SSWT to extract time–frequency characteristics that are sensitive to delamination. Next, extracted features are fed into deep CNN model VGG16 to develop a predictive model based on transfer learning. To enhance the generalization capability of the proposed model, modified whale optimization algorithm (MWOA) is utilized to optimize network hyperparameters during the training process. The performance of the proposed hybrid framework for delamination identification is validated using test data collected from the field testing of real bridges using GPR device. The proposed method demonstrates satisfactory results compared to other commonly used techniques, with the prediction accuracy of over 94%, providing an effective and efficient solution to the challenges of bridge defect detection.
