This paper investigates the Frequency Modulation Continuous Wave (FMCW) radar sensor for multi-target displacement measurement in Structural Health Monitoring (SHM). The principle of three-dimensional (3-D) displacement measurement of civil infrastructures is analyzed. The requirements of high-accuracy displacement and multi-target identification for the measuring sensors are discussed. The fundamental measuring principle of FMCW radar is presented with rigorous mathematical formulas, and further the multiple-target displacement measurement is analyzed and simulated. In addition, a FMCW radar prototype is designed and fabricated based on an off-the-shelf radar frontend and data acquisition (DAQ) card, and the displacement error induced by phase asynchronism is analyzed. The conducted outdoor experiments verify the feasibility of this sensing method applied to multi-target displacement measurement, and experimental results show that three targets located at different distances can be distinguished simultaneously with millimeter level accuracy.

Sensors have been installed on many civil infrastructures to monitor structural conditions. False alarm will be triggered, however, by the malfunctioned sensor even under normal conditions. Therefore, pre-processing is needed to identify and classify the sensor fault before structural damage detection and assessment. This paper proposes a deep learning-based method, namely, the Tsfresh Long Short-Term Memory networks (TLSTM), to address the sensor fault classification. The python package Tsfresh is used to extract features that are sensitive to sensor fault from measured signals. These features are further selected with the Benjamini–Yekutieli procedure. With the selected features, a long short-term memory (LSTM) network combining two fully-connected layers and a Softmax layer is constructed to differentiate sensor fault types. Experimental data with five types of sensor faults are obtained by mechanical and electrical simulation. The proposed method is shown able to successfully classify all these sensor fault types.

A few sensing techniques have been developed for detecting defects in civil infrastructures in structural health monitoring (SHM) instead of onsite human inspection. Many false alarms observed are due to faults in sensors, and some of them may lead to incorrect interpretation of the measured data. In this paper, the differentiation of different sources of variability, i.e. the sensor fault and structural damage, is investigated for a vibration-based SHM system. A deep learning-based method with a new architecture of Fully connected Stateful Long Short Time Memory (FS-LSTM) Neural Networks for differentiating the sensor fault and structural damage without information on details of the fault is proposed. Information from the sensor fault is assumed local, and that from structural damage is relatively global affecting several sensors. Since the LSTM are capable to learn data features automatically, the proposed method performs well without an accurate mathematical model. Stationary random acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured data with estimation on four types of sensor fault. The response residues between the true and predicted values of the deep FS-LSTM NN is statistically analyzed to determine the threshold of sensor fault. An experimental study with a steel frame in the laboratory is also performed to validate the proposed method.

A multivariate time-series analysis employing a state–space embedding strategy and singular value decomposition is presented in this article to detect infrastructure damage. After summarizing the current state–space reconstruction method, the univariate state–space reconstruction is extended to multivariate (or global) reconstruction for observed time series at multiple locations. Under the hypothesis that reconstructed phase state geometry will change with damage, a reduced feature based on Mahalanobis distance of the most significant singular value vector, which is calculated from the reconstructed trajectory, is proposed. Both the area under receiver operating characteristic curve and deflection coefficient are used as comparison metrics to illustrate the presence and severity of damage. The advantage of this proposed approach is computational efficiency and easy implementation using state–space methodology since it does not require high-dimensional neighbor searches, as previous methods have proposed. Validation of the approach is demonstrated using a 6-degree-of-freedom linear spring–mass system and the IASC-ASCE 4-story benchmark experimental structure. Results from both test beds show that damage occurrence and severity can be successfully identified.