The growing volume of available infrastructural monitoring data enables the development of powerful datadriven approaches to estimate infrastructure health conditions using direct measurements. This paper proposes a deep learning methodology to estimate infrastructure physical parameters, such as railway track stiffness, using drive-by vibration response signals. The proposed method employs a Long Short-term Memory (LSTM) feature extractor accounting for temporal dependencies in the feature extraction phase, and a bidirectional Long Short-term Memory (BiLSTM) networks to leverage bidirectional temporal dependencies in both the forward and backward paths of the drive-by vibration response in condition estimation phase. Additionally, a framing approach is employed to enhance the resolution of the monitoring task to the beam level by segmenting the vibration signal into frames equal to the distance between individual beams, centering the frames over the beam nodes. The proposed LSTM-BiLSTM model offers a versatile tool for various bridge and railway infrastructure conditions monitoring using direct drive-by vibration response measurements. The results demonstrate the potential of incorporating temporal analysis in the feature extraction phase and emphasize the pivotal role of bidirectional temporal information in infrastructure health condition estimation. The proposed methodology can accurately and automatically estimate railway track stiffness and identify local stiffness reductions in the presence of noise using drive-by measurements. An illustrative case study of vehicle-track interaction simulation is used to demonstrate the performance of the proposed model, achieving a maximum mean absolute percentage error of 1.7% and 0.7% in estimating railpad and ballast stiffness, respectively.

翻译：日益增长的基础设施监测数据量，使得利用直接测量数据开发强大的数据驱动方法来评估基础设施健康状况成为可能。本文提出一种深度学习方法，利用车载振动响应信号来估计基础设施物理参数，如铁路轨道刚度。所提方法在特征提取阶段采用长短期记忆（LSTM）特征提取器以考虑时间依赖性，并在状态估计阶段采用双向长短期记忆（BiLSTM）网络，以利用车载振动响应在正向和反向路径中的双向时间依赖性。此外，通过将振动信号分割成与单个梁间距相等的帧，并将帧中心对准梁节点，采用一种分帧方法将监测任务的分辨率提升至梁的水平。所提出的LSTM-BiLSTM模型为利用直接车载振动响应测量进行各类桥梁和铁路基础设施状态监测提供了一个通用工具。结果表明，在特征提取阶段纳入时间分析具有潜力，并强调了双向时间信息在基础设施健康状况评估中的关键作用。该方法能够在存在噪声的情况下，利用车载测量准确、自动地估计铁路轨道刚度并识别局部刚度下降。通过一个车辆-轨道相互作用仿真实例研究来展示所提模型的性能，在估计轨下垫层和道砟刚度时，分别实现了最大平均绝对百分比误差为1.7%和0.7%。