Verification of Tensile Force Estimation Method for Temporary Steel Rods of FCM Bridges Based on Area of Magnetic Hysteresis Curve Using Embedded Elasto-Magnetic Sensor

Kim, Won-Kyu; Kim, Junkyeong; Park, Jooyoung; Kim, Ju‐Won; Park, Seunghee

doi:10.3390/s22031005

Cited by 3 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…E/M sensors are a nondestructive measurement method suitable for monitoring tension members in structures, as their characteristics do not change over time and are highly accurate and reliable [ 13 , 14 , 15 ]. E/M sensors are highly accurate and reliable and have been extensively studied for managing and estimating tension losses in cables and steel rods in structures such as bridges, tunnels, and slopes [ 16 , 17 , 18 ]. However, the existing magnetization structure, the solenoid-type, has limitations in field usability because the coil must be wound by hand in the field.…”

Section: Introductionmentioning

confidence: 99%

Yoke-Type Elasto-Magnetic Sensor-Based Tension Force Monitoring Method for Enhancement of Field Applicability

Lee,

Kyung,

Kim

2024

Sensors

Self Cite

View full text Add to dashboard Cite

Tension members are key members that maintain stability and improve the strength of structures such as cable-stayed bridges, PSC structures, and slopes. Their application has recently been expanded to new fields such as mooring lines in subsea structures and aerospace fields. However, the tensile strength of the tension members can be abnormal owing to various risk factors that may lead to the collapse of the entire structure. Therefore, continuous tension monitoring is necessary to ensure structural safety. In this study, an improved elasto-magnetic (E/M) sensor was used to monitor tension force using a nondestructive method. General E/M sensors have limitations that make it difficult to apply them to operating tension members owing to their solenoid structure, which requires field winding. To overcome this problem, the magnetization part of the E/M sensor was improved to a yoke-type sensor, which was used in this study. For the development of the sensors, the numerical design and magnetization performance verification of the sensor were performed through eddy current solution-type simulations using ANSYS Maxwell. Using the manufactured yoke-type E/M sensor, the induced voltage signals according to the tension force of the specimen increasing from 0 to 10 tons at 1-ton intervals were repeatedly measured using DAQ with wireless communication. The measured signals were indexed using peak-to-peak value of induced voltages and used to analyze the signal change patterns as the tension increased. Finally, the analyzed results were compared with those of a solenoid-type E/M sensor to confirm the same pattern. Therefore, it was confirmed that the tension force of a tension member can be estimated using the proposed yoke-type E/M sensor. This is expected to become an effective tension monitoring technology through performance optimization and usability verification studies for each target tension member in the future.

show abstract

Section: Introductionmentioning

confidence: 99%

Yoke-Type Elasto-Magnetic Sensor-Based Tension Force Monitoring Method for Enhancement of Field Applicability

Lee,

Kyung,

Kim

2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…During the construction phase of the bridge using FCM, steel rods with large diameters such as 40 mm or larger are used as prestressed members. Furthermore, there is only one study analyzing the diference in induced voltage signals according to the change in tension between them [18]. However, the relationship between the features of magnetic hysteresis and environmental factors such as temperature was not analyzed theoretically.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, signal changes due to temperature changes during long-term monitoring were not considered though the result of tension estimation using EM sensors had practical accuracy. A few studies have considered the temperature for tension estimation using stress-measurement sensors based on a polynomial ftting method or artifcial intelligence (AI) [12,[18][19][20]. Because the studies were performed under specifc temperature conditions, variations in temperature from a long-term perspective were only partially considered.…”

Section: Introductionmentioning

confidence: 99%

“…Because the studies were performed under specifc temperature conditions, variations in temperature from a long-term perspective were only partially considered. Terefore, the results may not be reliable because the temperature and force-related data were ftted without identifying the efect of the temperature analytically [18,20]. Moreover, if artifcial neural networks (ANNs) are used without analyzing the efect of temperature properly, the number of training patterns, that is, the number of observations, can adversely afect the results [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Elastomagnetic Sensor-Based Long-Term Tension Monitoring of Prestressed Bridge Member with Temperature Compensation

Park,

Kim,

Jeon

et al. 2023

Structural Control and Health Monitoring

Self Cite

View full text Add to dashboard Cite

Continuous monitoring of the prestressed members of a bridge under construction using the free cantilever method (FCM) is crucial for ensuring bridge safety. Temperature-sensitive sensors require special considerations as they may misinterpret the signal and tension. Moreover, the unnecessary and inappropriate use of features obtained from the sensor signal can deteriorate the efficiency of the signal and, therefore, tension analysis. This study proposes a tension estimation method using an embedded elastomagnetic (EM) sensor with a temperature-compensation technique. Changes in the signal due to the tension in the temporary steel rods were analyzed using a full-scale test, and the sensor data were acquired for 15 months via the field application. The temperature effect on the signal could be removed by subtracting the tension from the signal using the thermistor data, reducing the error by 91.99% when considering permeability. Additionally, linear regression (LR) and machine learning (ML) algorithms were adopted to predict the tension. Furthermore, the performances of both algorithms were compared using mean absolute error (MAE) and R2. For the prediction using each feature in magnetic hysteresis, LR surpassed ML and the permeability exhibited the highest prediction performance. Meanwhile, predictions using multiple features were attempted to investigate the applicability of ML. Two cases of prediction were performed using ML: on using all the features and the other using three features excluding coercivity, which showed poor relevance to tension. As a result, the performance of the tension prediction was improved significantly compared to the results obtained by LR. In summary, the obtained results have demonstrated that the utilization of selective features of data with temperature compensation techniques could enhance predictive power.

show abstract

“…Kim et al [ 13 ] developed an embedded coil-based EM sensor to monitor the tensile force of pre-stressed tendons used in the concrete girders of bridges. Field test results demonstrated success in the linear characteristic of tensile force with the area of the B-H loop.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Indicators for Evaluating and Seeking Elasto-Magnetic Parameters for High-Performance Cable Force Monitoring

Shi

Wang

et al. 2022

Sensors

View full text Add to dashboard Cite

The elasto-magnetic method is a promising pathway for cable force monitoring in cable-stayed bridges. Under the action of an externally applied pulsed magnetic field, both the variation in the main flux recorded by the induction coil and the localized surface magnetic field measured by the packaged magnetic sensor are typical signals for observing the elasto-magnetic effect in tensioned cables. However, the performances of the parameters extracted from the two types of elasto-magnetic signals are never strictly compared in the experiment. Meanwhile, comprehensive indicators for evaluating the ability of elasto-magnetic parameters on cable force characterization are seldom discussed. As a result, it is difficult to compare the performances of elasto-magnetic devices developed by different teams, and the pathway of seeking new parameters for cable force monitoring is obstructed. In this study, elasto-magnetic calibration experiments were performed on a cable of seven-wire steel strands to simultaneously measure the variation in the main flux and the localized surface magnetic field. Comprehensive indicators considering sensitivity, hysteresis error, and cable force resolution are proposed to examine the performances of classic elasto-magnetic parameters and new candidate ones. Through comparative study, two new parameters demonstrated outstanding ability for cable force measurement, and they are the minimum amplitude of the induced voltage and the area under the curve between two points of 3 dB height of the voltage measured by a Hall sensor. The latter is recommended for high-performance cable force monitoring from the perspective of simplicity in sensor configuration.

show abstract

Verification of Tensile Force Estimation Method for Temporary Steel Rods of FCM Bridges Based on Area of Magnetic Hysteresis Curve Using Embedded Elasto-Magnetic Sensor

Cited by 3 publications

References 25 publications

Yoke-Type Elasto-Magnetic Sensor-Based Tension Force Monitoring Method for Enhancement of Field Applicability

Yoke-Type Elasto-Magnetic Sensor-Based Tension Force Monitoring Method for Enhancement of Field Applicability

Elastomagnetic Sensor-Based Long-Term Tension Monitoring of Prestressed Bridge Member with Temperature Compensation

Comprehensive Indicators for Evaluating and Seeking Elasto-Magnetic Parameters for High-Performance Cable Force Monitoring

Contact Info

Product

Resources

About