Ground penetrating radar-based automated defect identification of bridge decks: a hybrid approach

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.