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. However, it is hard to measure the residual tensile force of outdated PSC bridges because the PS tendons were buddied in the concrete and the any sensory systems were not installed. To measure the tensile force of the outdated PSC girder bridges, this study proposed an external magnetization based tensile force estimation method using external magnetization sensor(EMS). The magnetic hysteresis of PS tendons is changed according to the residual tensile force. To measure the magnetic hysteresis of outdated PSC girder, the EMS was designed to concentrate the magnetic field to the PS tendons which located inside of girder. The 8 magnetization coils were installed to the U shape frame which can cover the both side of PSC I-shape girder and the designed currents were inputted to each magnetization coils to concentrate magnetic field. The flux density of magnetized PS tendon was measured hall sensors which located at the frame. To verify the proposed method, the in-field tests were performed. The magnetic hysteresis of PSC girder with difference tensile forces were measured using EMS and the feature was extracted to estimate residual tensile force of PSC girder. The estimated residual tensile forces were compared with reference tensile forces measured by hydraulic jacking machine which installed anchorage of PSC girder specimen. According to the measurement results, the proposed method can be a one of the solutions to measure the residual tensile force of outdated PSC I-shape girder bridges.
Non-destructive testing on wire rope is in great demand to prevent safety accidents at sites where many heavy equipment using ropes are installed. In this paper, a research on quantification of magnetic flux leakage (MFL) signals were carried out to detect damages on wire rope. First, a simulation study was performed with a steel rod model using a finite element analysis (FEA) program. The leakage signals from the simulation study were obtained and it was compared for parameter: depth of defect. Then, an experiment on same conditions was conducted to verify the results of the simulation. Throughout the results, the MFL signal was quantified and a wire rope damage detection was then confirmed to be feasible. In further study, it is expected that the damage characterization of an entire specimen will be visualized as well.
Recently, novel methods to estimate the strength of concrete have been reported based on numerous NDT methods. Especially, electro-mechanical impedance technique using piezoelectric sensors are studied to estimate the strength of concrete. However, the previous research works could not provide the general information about the early-age strength important to manage the quality of concrete and/or the construction process. In order to estimate the early-age strength of concrete, the electro-mechanical impedance method and the artificial neural network(ANN) is utilized in this study. The electro-mechanical impedance varies with the mechanical properties of host structures. Because the strength development is most influential factor among the change of mechanical properties at early-age of curing, it is possible to estimate the strength of concrete by analyzing the change of E/M impedance. The strength of concrete is a complex function of several factors like mix proportion, temperature, elasticity, etc. Because of this, it is hard to mathematically derive equations about strength of concrete. The ANN can provide the solution about early-age strength of concrete without mathematical equations. To verify the proposed approach, a series of experimental studies are conducted. The impedance signals are measured using embedded piezoelectric sensors during curing process and the resonant frequency of impedance is extracted as a strength feature. The strength of concrete is calculated by regression of strength development curve obtained by destructive test. Then ANN model is established by trained using experimental results. Finally the ANN model is verified using impedance data of other sensors.
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