Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy.
MOX, Dipartimento di Matematica, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy.
Sensors (Basel). 2021 Jun 19;21(12):4207. doi: 10.3390/s21124207.
In civil engineering, different machine learning algorithms have been adopted to process the huge amount of data continuously acquired through sensor networks and solve inverse problems. Challenging issues linked to structural health monitoring or load identification are currently related to big data, consisting of structural vibration recordings shaped as a multivariate time series. Any algorithm should therefore allow an effective dimensionality reduction, retaining the informative content of data and inferring correlations within and across the time series. Within this framework, we propose a time series AutoEncoder (AE) employing inception modules and residual learning for the encoding and the decoding parts, and an extremely reduced latent representation specifically tailored to tackle load identification tasks. We discuss the choice of the dimensionality of this latent representation, considering the sources of variability in the recordings and the inverse-forward nature of the AE. To help setting the aforementioned dimensionality, the false nearest neighbor heuristics is also exploited. The reported numerical results, related to shear buildings excited by dynamic loadings, highlight the signal reconstruction capacity of the proposed AE, and the capability to accomplish the load identification task.
在土木工程中，已经采用了不同的机器学习算法来处理通过传感器网络连续获取的大量数据，并解决反问题。与结构健康监测或负载识别相关的具有挑战性的问题与大数据有关，大数据由结构振动记录组成，这些记录呈现为多元时间序列。因此，任何算法都应该允许有效的降维，保留数据的信息内容，并推断时间序列内和跨时间序列的相关性。在这个框架内，我们提出了一种使用 inception 模块和残差学习的时间序列自编码器 (AE)，用于编码和解码部分，以及一个特别针对负载识别任务定制的、极简化的潜在表示。我们讨论了这种潜在表示的维度选择，考虑了记录中的可变性来源以及 AE 的正反性质。为了帮助设置上述维度，还利用了虚假最近邻启发式。所报告的数值结果与动态载荷激励的剪切建筑物有关，突出了所提出的 AE 的信号重构能力，以及完成负载识别任务的能力。
