Lin Jun, Ma Hongwei
School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China.
Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies for Urban Lifeline Engineering, Dongguan 523808, China.
Sensors (Basel). 2024 Apr 19;24(8):2616. doi: 10.3390/s24082616.
Identifying the structural state without baseline data is an important engineering problem in the field of structural health monitoring, which is crucial for assessing the safety condition of structures. In the context of limited accelerometers available, this paper proposes a correlation-based damage identification method using Variational Autoencoder neural networks. The approach involves initially constructing a Variational Autoencoder network model for bridge damage detection, optimizing parameters such as loss functions and learning rates for the model, and ultimately utilizing response data from limited sensors for model training analysis to determine the structural state. The contribution of this paper lies in the ability to identify structural damage without baseline data using response data from a small number of sensors, reducing sensor costs and enhancing practical applications in engineering. The effectiveness of the proposed method is demonstrated through numerical simulations and experimental structures. The results show that the method can identify the location of damage under different damage conditions, exhibits strong robustness in detecting multiple damages, and further enhances the accuracy of identifying bridge structures.
在没有基线数据的情况下识别结构状态是结构健康监测领域中的一个重要工程问题，这对于评估结构的安全状况至关重要。在可用加速度计数量有限的情况下，本文提出了一种基于相关性的损伤识别方法，该方法使用变分自编码器神经网络。该方法首先构建用于桥梁损伤检测的变分自编码器网络模型，优化模型的损失函数和学习率等参数，最终利用有限传感器的响应数据进行模型训练分析，以确定结构状态。本文的贡献在于能够使用少量传感器的响应数据在没有基线数据的情况下识别结构损伤，降低了传感器成本并增强了在工程中的实际应用。通过数值模拟和实验结构验证了所提方法的有效性。结果表明，该方法能够在不同损伤条件下识别损伤位置，在检测多重损伤时具有很强的鲁棒性，进一步提高了桥梁结构识别的准确性。
