Over the past decade, the growing importance of machine learning-based structural health monitoring (SHM) for early-stage damage detection has become evident. Time series forecasting, using deep learning, has emerged as a key focus, significantly contributing to improving damage detection, localization, and quantification processes. Researchers in SHM have conducted numerous studies utilizing neural networks based on time series forecasting, grounded in traditional methods. This study diverges from existing research by directly incorporating neural network prediction errors in time series for detecting, localizing, and quantifying damage. The proposed methods are well-suited for online structural monitoring. They eliminate the need for data classification methods and damage-sensitive feature extraction techniques by relying solely on training the neural network with data from structurally sound conditions. However, the testing process does require data from damaged conditions. To address the non-linear and non-stationary characteristics of the signals, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method is applied for signal processing. This method processes response signals (i.e., time series) from three well-known benchmark structures: the University of Central Florida structure, the Qatar University Grandstand Simulator, and the Z24 Bridge. Subsequently, the first intrinsic mode function (IMF) obtained from signal decomposition is independently input into Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for time series prediction. Optimal parameter values for the LSTM and GRU neural networks are chosen using the Bayesian Optimization (BO) algorithm before the prediction process. By introducing three indices—Statistical Distance Function (SDF), error index, and accuracy index—the evaluation not only emphasizes the accuracy of the methods but also explores the localization and quantification of damage. The results demonstrate that both ICEEMDAN-BO-LSTM-SDF and ICEEMDAN-BO-GRU-SDF methods have successfully achieved accurate detection, localization, and quantification without the need for data classification and damage-sensitive feature extraction methods, and merely by utilizing data from healthy states for neural network training.

中文翻译：

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利用时间序列预测误差的智能混合方法增强结构健康监测


在过去十年中，基于机器学习的结构健康监测 （SHM） 在早期损伤检测中的重要性日益凸显。使用深度学习进行时间序列预测已成为一个重点，为改进损害检测、定位和量化过程做出了重大贡献。SHM 的研究人员利用基于时间序列预测的神经网络进行了大量研究，这些研究基于传统方法。这项研究与现有研究不同，直接将神经网络预测误差纳入时间序列中，以检测、定位和量化损伤。所提出的方法非常适合在线结构监测。它们完全依赖于使用来自结构良好条件的数据来训练神经网络，从而消除了对数据分类方法和损伤敏感特征提取技术的需求。但是，测试过程确实需要来自损坏条件的数据。为了解决信号的非线性和非平稳特性，采用改进的自适应噪声完全集成经验模态分解 （ICEEMDAN） 方法进行信号处理。该方法处理来自三个著名基准结构的响应信号（即时间序列）：中佛罗里达大学结构、卡塔尔大学看台模拟器和 Z24 桥。随后，从信号分解中获得的第一个本征模态函数 （IMF） 独立输入到长短期记忆 （LSTM） 和门控循环单元 （GRU） 神经网络中，用于时间序列预测。在预测过程之前，使用贝叶斯优化 （BO） 算法选择 LSTM 和 GRU 神经网络的最佳参数值。 通过引入三个指数（统计距离函数 （SDF）、误差指数和精度指数），评估不仅强调了方法的准确性，还探索了损伤的定位和量化。结果表明，ICEEMDAN-BO-LSTM-SDF 和 ICEEMDAN-BO-GRU-SDF 方法都成功地实现了准确的检测、定位和量化，而无需数据分类和损伤敏感特征提取方法，只需利用健康状态的数据进行神经网络训练。