Research Institute of Construction & Environmental System, Inha University, Incheon 22212, Korea.
Advanced Railroad Civil Engineering Division, Korea Railroad Research Institute, Uiwang 16105, Korea.
Sensors (Basel). 2022 Feb 25;22(5):1839. doi: 10.3390/s22051839.
Deep learning has been widely employed in recent studies on bridge-damage detection to improve the performance of damage-detection methods. Unsupervised deep learning can be effectively utilized to increase the applicability of damage-detection approaches. Hence, the authors propose a convolutional-autoencoder (CAE)-based damage-detection approach, which is an unsupervised deep-learning network. However, the CAE-based damage-detection approach demonstrates only satisfactory accuracy for prestressed concrete bridges with a single-vehicle load. Therefore, this study was performed to verify whether the CAE-based damage-detection approach can be applied to bridges with multi-vehicle loads, which is a typical scenario. In this study, rigid-frame and reinforced-concrete-slab bridges were modeled and simulated to obtain the behavior data of bridges. A CAE-based damage-detection approach was tested on both bridges. For both bridges, the results demonstrated satisfactory damage-detection accuracy of over 90% and a false-negative rate of less than 1%. These results prove that the CAE-based approach can be successfully applied to various types of bridges with multi-vehicle loads.
深度学习在桥梁损伤检测研究中得到了广泛应用，以提高损伤检测方法的性能。无监督深度学习可以有效地提高损伤检测方法的适用性。因此，作者提出了一种基于卷积自动编码器（CAE）的损伤检测方法，这是一种无监督深度学习网络。然而，基于 CAE 的损伤检测方法对于单车载荷的预应力混凝土桥梁仅具有令人满意的准确性。因此，本研究旨在验证基于 CAE 的损伤检测方法是否可应用于多车载荷的桥梁，这是一种典型的情况。在本研究中，对刚架桥和钢筋混凝土板桥进行建模和模拟，以获得桥梁的行为数据。在这两种桥梁上都测试了基于 CAE 的损伤检测方法。对于这两种桥梁，结果均证明了超过 90%的损伤检测准确率和小于 1%的假阴性率，这表明基于 CAE 的方法可以成功应用于具有多车载荷的各种类型的桥梁。
