Fiber Bragg grating (FBG) sensors have attracted a considerable amount of interest for their superior characteristics. However, the FBG sensors made on bare fibers are easily damaged. For their safe use in engineering, the glass core of optical fibers is coated with softer low modulus protective coatings. A portion of the host material strain is absorbed by the protective coatings when the strain transfers from the host material to the fiber core, and hence only a segment of structural strain is sensed. By introducing the shear modulus of the host material, a novel analytical model is developed for evaluating the sensing strain of the embedded FBG sensors in composite structures based on the strain in a host material. The average strain transfer ratio is deduced to describe the percentage transferred to the optical fiber core from the host material. It is concluded that the shear modulus of the host material influences strain transmission, especially when it is much lower than the modulus of the fiber cor...

Pipeline leakage and corrosion are two serious threats to pipeline safety operation. Therefore, to ensure the safety operation of long-distance pipeline, it is of great significance to conduct pipeline monitoring. Since hoop strain is an effective indicator to reflect the inner pressure fluctuation and the wall thickness reduction of the pipeline, a method of monitoring leakage and corrosion simultaneously was proposed based on hoop strain measurement. In order to test the hoop strain variation, this paper introduces a fiber Bragg grating (FBG) strain hoop sensor. To verify the monitoring method and the performance of this FBG strain sensor, a pipeline leakage simulation experiment and corrosion simulation experiment were conducted on an actual pipeline and a polyvinyl chloride (PVC) pipe model, respectively. The experimental results demonstrate that the pipeline leakage and corrosion can be detected by the FBG hoop strain sensor. The FBG strain hoop sensor is a promising device in pipeline safety monitoring.

Model updating is an effective means of damage identification and surrogate modeling has attracted considerable attention for saving computational cost in finite element (FE) model updating, especially for large-scale structures. In this context, a surrogate model of frequency is normally constructed for damage identification, while the frequency response function (FRF) is rarely used as it usually changes dramatically with updating parameters. This paper presents a new surrogate model based model updating method taking advantage of the measured FRFs. The Frequency Domain Assurance Criterion (FDAC) is used to build the objective function, whose nonlinear response surface is constructed by the Kriging model. Then, the efficient global optimization (EGO) algorithm is introduced to get the model updating results. The proposed method has good accuracy and robustness, which have been verified by a numerical simulation of a cantilever and experimental test data of a laboratory three-story structure.

In this paper, a non-modal parametric method to identify structural damage using a regularized autoregressive moving average time series model under environmental excitation is proposed in combination with the virtual impulse response function. This method can use the structural vibration response to determine the damage caused to the structure during environmental excitation. Firstly, the virtual impulse response function is obtained by using the structural vibration response. Then, a regularized ARMA time series model is used to fit the virtual impulse response function. Based on the change of auto-regression coefficients in the regularization model under different damage cases, the structural damage is identified. The authors derive the regularization equation of an ARMA time series model to solve the problems in a time series model and obtain the regularization coefficient. Finally, this method is applied to a three-degrees-of-freedom chain structure and a three-floor shear structure of the Los Alamos National Laboratory (LANL). The experimental results show that the method based on the regularized ARMA time series model under environmental excitation can effectively identify the structural damage, which is a reliable method for damage identification. The regularized ARMA time series model can accurately extract signal features and has invaluable application prospects in the field of structural health monitoring.