The intelligent identification of grouting defects in grouted sleeves in prefabricated structures is critical for maintaining structural integrity. However, current deep learning-based identification methods face limitations, including insufficient model adaptability and the difficulty of obtaining labeled data. Models trained on one domain struggle to generalize to others due to differences in data distributions, making these methods challenging to apply in real-world scenarios. To address this engineering challenge, this paper investigates the applicability of maximum mean discrepancy-based domain adaptation (MMD-based DA) and domain adversarial training (DAT) approaches for cross-domain grouting defect identification. Acceleration signals collected by accelerometers near the grouted sleeves are used as the model input. The model’s ability to generalize across domains is evaluated by training on labeled data from one working condition and testing its performance on other working conditions using only unlabeled data. And these methods are compared with traditional Convolutional Neural Networks (CNNs). Experiments were conducted on a two-layer prefabricated frame structure. The experimental results demonstrated the effectiveness of the MMD-based DA method in improving the accuracy and robustness of defect identification across different domains, with the use of unlabeled data.
Xie et al. (Mon,) studied this question.