Yang Libo, Zhu Dawei, Liu Xuemei
Advanced Research Institute for Digital-Twin Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Sci Rep. 2024 Dec 28;14(1):31277. doi: 10.1038/s41598-024-82612-3.
Traditional hydraulic structures rely on manual visual inspection for apparent integrity, which is not only time-consuming and labour-intensive but also inefficient. The efficacy of deep learning models is frequently constrained by the size of available data, resulting in limited scalability and flexibility. Furthermore, the paucity of data diversity leads to a singular function of the model that cannot provide comprehensive decision support for improving maintenance measures. This paper proposes an efficacious methodology for identifying diverse apparent damages in hydraulic structures to address the limitations of existing technologies. The advanced features of apparent damage in hydraulic structures were elucidated by fine-tuning the top-level parameters of the lightweight pre-trained model, thereby mitigating the data dependency issue inherent in the model. Ensemble learning algorithms are employed to classify high-dimensional samples to enhance the accuracy and stability of the classification. However, ensemble learning algorithms are subject to time consuming issues when applied to high-dimensional datasets. To this end, we propose a robust discriminative feature selection model to identify the most salient features, thereby enhancing the performance of apparent damage recognition in hydraulic structures while concurrently reducing the inference time. The results demonstrated that the accuracies of this method in identifying crack, fracture, hole and normal structures were 87.65%, 87.82%, 96.99%, and 95.25%, respectively. This methodology exhibits significant applicability and practical value for the intelligent inspection of hydraulic structures.
传统水工建筑物依靠人工目视检查来判断表面完整性，这不仅耗时费力，而且效率低下。深度学习模型的效能常常受到可用数据规模的限制，导致可扩展性和灵活性有限。此外，数据多样性的匮乏导致模型功能单一，无法为改进维护措施提供全面的决策支持。本文提出一种有效的方法来识别水工建筑物中的各种表面损伤，以解决现有技术的局限性。通过微调轻量级预训练模型的顶层参数，阐明了水工建筑物表面损伤的高级特征，从而缓解了模型中固有的数据依赖问题。采用集成学习算法对高维样本进行分类，以提高分类的准确性和稳定性。然而，集成学习算法应用于高维数据集时存在耗时问题。为此，我们提出一种强大的判别特征选择模型来识别最显著的特征，从而提高水工建筑物表面损伤识别的性能，同时减少推理时间。结果表明，该方法在识别裂缝、断裂、孔洞和正常结构时的准确率分别为87.65%、87.82%、96.99%和95.25%。该方法在水工建筑物智能检测中具有显著的适用性和实用价值。
