Machine Learning based Tensile Force Estimation for PSC Girder using Embedded EM Sensor

Abstract

The actual tensile force of pre-stressed (PS) tendons of a pre-stressed concrete (PSC) girder is one of the important factors for evaluating the performance of PSC girder bridges. To measure the tensile force of the PS tendon, this study proposed a machine learning based tensile force estimation method using embedded elasto-magnetic (EM) sensors. The magnetic hysteresis of PS tendons are changed according to the applied tensile force. To measure the magnetic hysteresis of PS tendon of PSC girder, the EM sensor should be embedded in the PSC girder because the PS tendons were located in inside of PSC girder. The radial basis function network (RBFN), one of the machine learning method, was used to estimate the tensile force using the variations in magnetic hysteresis. To verify the proposed method, the in-field tests were performed. The embedded EM sensors were embedded into PSC girder specimen and the magnetic hysteresis changes due to the variations in tensile forces were measured using embedded EM sensors. The tensile forces were estimated using trained RBFN and they compared with reference tensile forces measured by hydraulic jacking machine. According to the measurement results, the proposed method can be a one of the solution to monitor the tensile force of PS tendons.