Monitoring of prestress and bond stress of self-sensing FBG steel strand

Zhu, Wanxu; Shen, Quanxi; Qin, Heying

doi:10.1016/j.measurement.2021.109246

Cited by 19 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te current techniques for manufacturing smart steel strands mainly include fusing FBG sensor and fber reinforced polymer (FRP) material into a whole by heating cure processing [46-48, 50, 51, 53, 54, 57, 58] and embedding FBG sensor into a hollow steel tube and injecting epoxy glue into the tube to achieve a reliable bond between the FBG and the tube [44,49,55]. In addition, Zhu et al [40] proposed a new method that encapsulating the FBG sensor in a longitudinal groove of the core wire, which allows FBG and core wire to be deformed synchronously.…”

Section: Review Of Various Prestress Evaluation Methodsmentioning

confidence: 99%

“…As for prestress monitoring, the most direct and efective way is to install sensors, such as load cells and strain gauges, during construction state [38,39], but such sensors are easily damaged and with low measuring accuracy. Embedded FBG sensors [40] and ME sensors [41], which have been rapidly developed in recent years, are considered efective means to monitor prestress loss due to their high accuracy and durability. Furthermore, using lead zirconate titanate (PZT) sensors [42], which are able to monitor the variation of EMI due to prestress loss, is also a viable approach.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nondestructive Testing and Health Monitoring Techniques for Structural Effective Prestress

Jia,

Zhang,

et al. 2023

Structural Control and Health Monitoring

View full text Add to dashboard Cite

Prestressed structures are widely employed in bridges and large-span spatial structures, and the accurate evaluation of prestress state is of great importance for structural maintenance. This paper reviews the nondestructive testing (NDT) and health monitoring techniques for structural effective prestress. Specifically, the fiber Bragg grating (FBG) sensor-based, magnetic-elastic (ME) sensor-based, dynamic response-based, ultrasonic guided wave (UGW)-based, electromechanical impedance (EMI)-based, and electrical resistance-based methods are reviewed in this paper. Firstly, the principle, application range, and measuring accuracy of each technique are introduced and analyzed, and the benefits and limitations of each technique are summarized: The FBG sensor and ME sensor take on high measuring accuracy and have been applied in practical engineering, but they are required to be preinstalled during structural construction; the dynamic response-based method is greatly effective in cable force assessment but not suitable for prestress evaluation of prestressed concrete (PSC) structures; the UGW-based, EMI-based, and electrical resistance-based methods have shown favorable potential for prestress assessment in laboratory experiments, but their feasibility and accuracy in practical engineering need to be verified. Secondly, the challenges and discussion of each method are discussed in the following four aspects: measuring range, reliability of measuring results, stability and durability considering long-term monitoring, and cost-efficiency. Finally, a decision tree is proposed to choose the most appropriate prestress evaluation method in a specific application scenario.

show abstract

Section: Review Of Various Prestress Evaluation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nondestructive Testing and Health Monitoring Techniques for Structural Effective Prestress

Jia,

Zhang,

et al. 2023

Structural Control and Health Monitoring

View full text Add to dashboard Cite

show abstract

“…In recent years, machine learning, as an important algorithm, has been widely used in the fields of signal processing and spectral recognition. [6][7][8][9][10] Liu TY et al 11 A classification and recognition method based on infrared spectrum is proposed. Back propagation (BP) neural network has advantages for infrared spectrum recognition, but it is only suitable for low-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Research on a quality feature recognition method of fiber Bragg grating reflectance spectrum

Jiang¹,

Zhang²,

Wu³

2023

International Conference on Optoelectronic Information and Functional Materials (OIFM 2023)

View full text Add to dashboard Cite

The quality of the reflection spectrum of the Fiber Bragg Grating (FBG) will directly affect the performance of the grating sensor network. Aiming at the problem of quality detection of fiber Bragg grating sensor network prepared online, this paper proposes a reflection spectrum quality identification algorithm based on Support Vector Machine (SVM). Firstly, the characteristic information of the original reflection spectrum is extracted based on theory and simulation. Then the raw feature information is preprocessed using a "signal segmentation and search model" to find the approximate locations of the original reflection spectrum. Finally, the kernel function is used to optimize the SVM network model until the recognition accuracy of the reflection spectrum quality feature meets the requirements. 2000 groups of reflection spectrum characteristic data were put into SVM recognition network for training, the recognition effect reached 99.9%. Finally, the non-uniform temperature field is established, and the experimental results show that the algorithm can identify the distortion spectrum pattern of the fiber Bragg grating array with more than 99% accuracy. It effectively improves the recognition accuracy of reflection spectrum peak sag and distortion and provides a new idea for the quality detection of core components of fiber Bragg grating sensor network.

show abstract

“…Meanwhile, fatigue performance [26], corrosion resistance property, and life prediction [27] were systematically investigated. This technology was applied to make the self-sensing steel strands embedding FBG sensor which was used for the health monitoring of internal and external losses of pre-stress, cables, rock, and soil anchor cables [28,29]. The change in temperature affected not only the embedded FBG wavelength variation but also the internal forces of the components (stress and strain) owing to the structures being hyperstatic.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Strain-Temperature Cross-Sensitivity of FBG Strain Sensors Embedded Onto Different Substrates

et al. 2022

Self Cite

View full text Add to dashboard Cite

The strain-temperature cross-sensitivity problem easily occurs in the engineering strain monitoring of the self-sensing embedded with fiber Bragg grating (FBG) sensors. In this work, a theoretical investigation of the strain-temperature cross-sensitivity has been performed using the temperature reference grating method. To experimentally observe and theoretically verify the problem, the substrate materials, the preloading technique, and the FBG initial central wavelength were taken as main parameters. And a series of sensitivity coefficients calibration tests and temperature compensation tests have been designed and carried out. It was found that when the FBG sensors were embedded on different substrates, their coefficients of the temperature sensitivity were significantly changed. Besides, the larger the coefficients of thermal expansion (CTE) of substrates were, the higher the temperature sensitivity coefficients would be. On the other hand, the effect of the preloading technique and FBG initial wavelength was negligible on both the strain monitoring and temperature compensation. In the case of similar substrates, we did not observe any difference between temperature sensitivity coefficients of the temperature compensation FBG with one free end or two free ends. The curves of the force along with temperature were almost overlapped with minor differences (less than 1%) gained by FBG sensors and pressure sensors, which verified the accuracy of the temperature compensation method. We suggest that this work can provide efficient solutions to the strain-temperature cross-sensitivity for engineering strain monitoring with the self-sensing element embedded with FBG sensors.

show abstract

Monitoring of prestress and bond stress of self-sensing FBG steel strand

Cited by 19 publications

References 37 publications

Nondestructive Testing and Health Monitoring Techniques for Structural Effective Prestress

Nondestructive Testing and Health Monitoring Techniques for Structural Effective Prestress

Research on a quality feature recognition method of fiber Bragg grating reflectance spectrum

Investigation of Strain-Temperature Cross-Sensitivity of FBG Strain Sensors Embedded Onto Different Substrates

Contact Info

Product

Resources

About