In order to research the field sensing characteristic of the carbon fiber smart material, the Tikhonov regularization principle and the modified Newton-Raphson(MNR) algorithm were adopted to solve the inverse problem ...In order to research the field sensing characteristic of the carbon fiber smart material, the Tikhonov regularization principle and the modified Newton-Raphson(MNR) algorithm were adopted to solve the inverse problem of the electrical resistance tomography(ERT). An ERT system of carbon fiber smart material was developed. Field sensing characteristic was researched with the experiment. The experimental results show that the specific resistance distribution of carbon fiber smart material is highly consistent with the distribution of structural strain. High resistance zone responds to high strain area, and the specific resistance distribution of carbon fiber smart material reflects the distribution of sample strain in covering area. Monitoring by carbon fiber smart material on complicated strain status in sample field domain is realized through theoretical and experimental study.展开更多
According to the principle of electrical resistance tomography （ ERT）, the resistivity distribution of the carbon fiber reinforced concrete （CFRC） in the sensing field can be measured by injecting exciting current...According to the principle of electrical resistance tomography （ ERT）, the resistivity distribution of the carbon fiber reinforced concrete （CFRC） in the sensing field can be measured by injecting exciting current and measuring the voltage on the sensor electrode arrays installed on the surface of the object. Therefore, measurement of the resistivity distribution of CFRC is divided into first measuring the boundary conditions and then inversely computing the resistivity distribution. To reach this goal, an ERT system was constructed, which is composed of a sensor array unit, a data acquisition unit and an image reconstruction unit. Simulations of static ERT was performed on set-ups with many objects spread in a homogeneous background, and a simulation of dynamic ERT was also done on a rectangular board, the resistivity of which was changed within a small domain of it. Then, the resistivity distribution of a CFRC sample with a circlar hole as the target was detected by the ERT system. Simulation and experimental results show that the reconstructed ERT image reflects the resistivity distribution or the resistivity change of CFRC structure well. Especially, a small change in resistivity can be identified from the reconstructed images in the simulation of dynamic ERT images.展开更多
基金Funded by the National High-tech Research and Development Program of China(863 Program)(No.2013AA031306)
文摘In order to research the field sensing characteristic of the carbon fiber smart material, the Tikhonov regularization principle and the modified Newton-Raphson(MNR) algorithm were adopted to solve the inverse problem of the electrical resistance tomography(ERT). An ERT system of carbon fiber smart material was developed. Field sensing characteristic was researched with the experiment. The experimental results show that the specific resistance distribution of carbon fiber smart material is highly consistent with the distribution of structural strain. High resistance zone responds to high strain area, and the specific resistance distribution of carbon fiber smart material reflects the distribution of sample strain in covering area. Monitoring by carbon fiber smart material on complicated strain status in sample field domain is realized through theoretical and experimental study.
基金The National Natural Science Foundation of China (No.50238040)
文摘According to the principle of electrical resistance tomography （ ERT）, the resistivity distribution of the carbon fiber reinforced concrete （CFRC） in the sensing field can be measured by injecting exciting current and measuring the voltage on the sensor electrode arrays installed on the surface of the object. Therefore, measurement of the resistivity distribution of CFRC is divided into first measuring the boundary conditions and then inversely computing the resistivity distribution. To reach this goal, an ERT system was constructed, which is composed of a sensor array unit, a data acquisition unit and an image reconstruction unit. Simulations of static ERT was performed on set-ups with many objects spread in a homogeneous background, and a simulation of dynamic ERT was also done on a rectangular board, the resistivity of which was changed within a small domain of it. Then, the resistivity distribution of a CFRC sample with a circlar hole as the target was detected by the ERT system. Simulation and experimental results show that the reconstructed ERT image reflects the resistivity distribution or the resistivity change of CFRC structure well. Especially, a small change in resistivity can be identified from the reconstructed images in the simulation of dynamic ERT images.
