Study of a steel strand tension sensor with difference single bypass excitation structure based on the magneto-elastic effect

Tang, Dedong; Huang, Shanglian; Chen, Weimin; Jiang, Jianshan

doi:10.1088/0964-1726/17/2/025019

Cited by 24 publications

(15 citation statements)

References 4 publications

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“…According to the magnetoelastic effect, the magnetic permeability of the steel strand varied with its stress. Explained by the famous joule theory [21], the relationship between the stress and the magnetization can be expressed as Equation (2). In Equation 2, σ is the stress of the steel strand, E the Young's modulus of the steel strand, λ s the axial strain constant of the steel strand, M s the saturation magnetization of the steel strand, K u the anisotropy constant of the uniaxial magnetization of the steel strand, θ 0 the angle between the easy magnetization axis of the steel strand and the direction of the magnetic field, and ΔM the change in the magnetization of the steel strand.…”

Section: Stress Monitoring Principlementioning

confidence: 99%

Stress Monitoring of Prestressed Steel Strand Based on Magnetoelastic Effect Under Weak Magnetic Field Considering Material Strain

Liu¹,

Zhang²,

Qu³

et al. 2020

PIER C

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Prestressed steel strands are critical components of prestressed structures, which determine the bearing capacity of the structures. The prestress loss of steel strands causes the bearing capacity to decline. To monitor the stress of prestressed steel strands, a stress monitoring method based on the magnetoelastic effect was proposed. The influence of the material strain was considered to improve monitoring accuracy. To do the monitoring, a coil-based sensor, using a small excitation current to generate a necessary magnetic field, was employed. The sensor converted the stress into inductance. An experimental system was set up, and two batches of specimens were tested. The experimental results showed that the measured inductance was stable and repeatable. There was a nonlinear relationship between the inductance and the stress. Strands of different batches need to be calibrated separately to obtain the inductance-stress equation. Based on the calibration equation and measured inductance, the stress of strands could be calculated. The difference between the calculated stress and the actual stress was small. Besides, to improve the accuracy and ease of the construction, the self-induction coil of the senor should be one layer and with moderate turns.

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Section: Stress Monitoring Principlementioning

confidence: 99%

Stress Monitoring of Prestressed Steel Strand Based on Magnetoelastic Effect Under Weak Magnetic Field Considering Material Strain

Liu¹,

Zhang²,

Qu³

et al. 2020

PIER C

View full text Add to dashboard Cite

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“…Monitoring of cable stress/force of in-service structures is challenging but crucial to the evaluation of structural safety [ 1 , 2 ]. The elasto-magnetic (EM) effect-based sensors have been receiving increasing attention, with superiorities of noncontact measurement, corrosion resistance, actual-stress measurement, low cost, and long service-life [ 3 , 4 , 5 , 6 ]. Owing to the EM effect (also known as the magneto-elastic effect), the action of the stress on the steel member would result in changes in the magnetic properties of the ferromagnetic materials and, thus, in the distribution of the magnetic field in the nearby area.…”

Section: Introductionmentioning

confidence: 99%

“…The hardware compensation method uses symmetric structures, or electronic circuits and components for compensation. For example, Tang et al devised an EM effect-based sensor with a differential structure using single bypass excitation to compensate for temperature effects in the tension monitoring of steel strands [ 4 , 11 ]. But hardware compensation methods usually suffer from limited measurement accuracy, poor reliability, and poor flexibility owing to manufacturing tolerances, or electronic devices drift, which restrict their applications in engineering practice.…”

Section: Introductionmentioning

confidence: 99%

Temperature Compensation of Elasto-Magneto-Electric (EME) Sensors in Cable Force Monitoring Using BP Neural Network

Zhang

Duan

Yang

et al. 2018

Sensors

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Techniques based on the elasto-magnetic (EM) effect have been receiving increasing attention for their significant advantages in cable stress/force monitoring of in-service structures. Variations in ambient temperature affect the magnetic behaviors of steel components, causing errors in the sensor and measurement system results. Therefore, temperature compensation is essential. In this paper, the effect of temperature on the force monitoring of steel cables using smart elasto-magneto-electric (EME) sensors was investigated experimentally. A back propagation (BP) neural network method is proposed to obtain a direct readout of the applied force in the engineering environment, involving less computational complexity. On the basis of the data measured in the experiment, an improved BP neural network model was established. The test result shows that, over a temperature range of approximately −10 °C to 60 °C, the maximum relative error in the force measurement is within ±0.9%. A polynomial fitting method was also implemented for comparison. It is concluded that the method based on a BP neural network can be more reliable, effective and robust, and can be extended to temperature compensation of other similar sensors.

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“…In 2008, Wang verified the capability of EM sensing technology for long-term structural health monitoring of the external tendons of a double-box girder bridge [ 14 ]. In the same year, Tang et al designed a new EM sensor that performs temperature compensation in a wide temperature range [ 15 ]. The use of EM sensors for the detection of creep in ferromagnetic materials was examined by Polar et al, in 2010 [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Calibration of Elasto-Magnetic Sensors on In-Service Cable-Stayed Bridges for Stress Monitoring

Cappello

Zonta

Laasri

et al. 2018

Sensors

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The recent developments in measurement technology have led to the installation of efficient monitoring systems on many bridges and other structures all over the world. Nowadays, more and more structures have been built and instrumented with sensors. However, calibration and installation of sensors remain challenging tasks. In this paper, we use a case study, Adige Bridge, in order to present a low-cost method for the calibration and installation of elasto-magnetic sensors on cable-stayed bridges. Elasto-magnetic sensors enable monitoring of cable stress. The sensor installation took place two years after the bridge construction. The calibration was conducted in two phases: one in the laboratory and the other one on site. In the laboratory, a sensor was built around a segment of cable that was identical to those of the cable-stayed bridge. Then, the sample was subjected to a defined tension force. The sensor response was compared with the applied load. Experimental results showed that the relationship between load and magnetic permeability does not depend on the sensor fabrication process except for an offset. The determination of this offset required in situ calibration after installation. In order to perform the in situ calibration without removing the cables from the bridge, vibration tests were carried out for the estimation of the cables’ tensions. At the end of the paper, we show and discuss one year of data from the elasto-magnetic sensors. Calibration results demonstrate the simplicity of the installation of these sensors on existing bridges and new structures.

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Study of a steel strand tension sensor with difference single bypass excitation structure based on the magneto-elastic effect

Cited by 24 publications

References 4 publications

Stress Monitoring of Prestressed Steel Strand Based on Magnetoelastic Effect Under Weak Magnetic Field Considering Material Strain

Stress Monitoring of Prestressed Steel Strand Based on Magnetoelastic Effect Under Weak Magnetic Field Considering Material Strain

Temperature Compensation of Elasto-Magneto-Electric (EME) Sensors in Cable Force Monitoring Using BP Neural Network

Calibration of Elasto-Magnetic Sensors on In-Service Cable-Stayed Bridges for Stress Monitoring

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