Intelligent Tension Correction Method for EME Sensors considering Torsion Effect of Wire Rope Suspender Cables

Abstract

Long-term and accurate monitoring of suspender cable tensions is particularly important for safe evaluation of cable suspension bridges or tied-arch bridges. Torsional deformation, commonly present in wire rope suspender cables (WR cables) during tensioning construction or in-service, has not been considered in the elasto-magneto-electric (EME) sensor system. This study investigated the effects of torsion on tension measurement and proposed an intelligent correction method without measuring the torsion angles per unit length. A calibration platform for full-scale WR cable is established with a rotation angle fixing device. Tension calibration experiments were carried out under free rotation condition without activating the angle fixing device and under various fixed rotation conditions by setting a series of initial fixed angles at the anchor head. It was found that the relative error for the EME sensor using the traditional calibration method under the free rotation condition could reach 11.72%. To improve the accuracy, an intelligent tension correction method for the torsion effect is proposed, which uses the experimental signals in various fixed conditions and the backpropagation neural network with K-fold cross-validation. The parameters of the BPNN were optimized by genetic algorithm, and it was found that the maximum relative error decreases from 11.72% to 5.24% and the maximum absolute error decreases from 21.75 kN to 14.67 kN for the condition of free rotation. Finally, the EME sensor with intelligent tension correction method was applied to a real suspension bridge. The measurement relative error of the field test decreases from 6.60% without the torsion compensation to 2.80% with the torsion compensation, which indicate that the proposed intelligent tension correction method can ensure the accurate tension measurement of the WR cables by the EME sensor.