Research

28 posts
Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion-1

Structural health monitoring (SHM) systems provide opportunities to understand the structural behaviors remotely in real-time. However, anomalous measurement data are frequently collected from structures, which greatly affect the results of further analyses. Hence, detecting anomalous data is crucial for SHM systems. In this article, we present a simple yet efficient approach that incorporates complementary information obtained from multi-view local binary patterns (LBP) and random forests (RF) to distinguish data anomalies. Acceleration data are first converted into gray-scale image data. The LBP texture features are extracted in three different views from the converted images, which are further aggregated as the anomaly representation for the final RF prediction. Consequently, multiple types of data anomalies can be accurately identified. Extensive experiments validated on an acceleration dataset acquired on a …

Anomaly Data Detection Extreme Event Identification Structural Health Monitoring, SHM

Automated seismic event detection considering faulty data interference using deep …

In this study, we propose a machine‐learning‐based approach to identify the modal parameters of the output‐only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independent feature of each mode, we use the principle of unsupervised learning, turning the training process of the neural network into the process of modal separation. A self‐coding neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then, we use a complex loss function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The neural …

Data Science and Engineering Structural Health Monitoring, SHM

Machine-learning-based methods for output-only structural modal identification