A Hybrid Method Applied to Improve the Efficiency of Full-Waveform Inversion for Pavement Characterization
AbstractGround penetrating radar (GPR), as a nondestructive testing tool, is suitable for estimating the thickness and permittivity of layers within the pavement. However, it would become problematic when the layer is thin with respect to the probing pulse width, in which case overlapping between the reflected pulses occurs. In order to deal with this problem, a hybrid method based on multilayer perceptrons (MLPs) and a local optimization algorithm is proposed. This method can be divided into two stages. In the first stage, the MLPs roughly estimate the thickness and the permittivity of the GPR signal. In the second stage, these roughly estimated values are used as the initial solution of the full-waveform inversion algorithm. The hybrid method and the conventional global optimization algorithm are respectively used to perform the full-waveform inversion of the simulated GPR data. Under the same inversion precision, the objective function needs to be calculated for 450 times and 30 times for the conventional method and the hybrid method, respectively. The hybrid method is also applied to a measured data, and the thickness estimation error is 1.2 mm. The results show the high efficiency and accuracy of such hybrid method to resolve the problem of estimating the thickness and permittivity of a “thin layer”. View Full-Text
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Zhang, J.; Ye, S.; Yi, L.; Lin, Y.; Liu, H.; Fang, G. A Hybrid Method Applied to Improve the Efficiency of Full-Waveform Inversion for Pavement Characterization. Sensors 2018, 18, 2916.
Zhang J, Ye S, Yi L, Lin Y, Liu H, Fang G. A Hybrid Method Applied to Improve the Efficiency of Full-Waveform Inversion for Pavement Characterization. Sensors. 2018; 18(9):2916.Chicago/Turabian Style
Zhang, Jingwei; Ye, Shengbo; Yi, Li; Lin, Yuquan; Liu, Hai; Fang, Guangyou. 2018. "A Hybrid Method Applied to Improve the Efficiency of Full-Waveform Inversion for Pavement Characterization." Sensors 18, no. 9: 2916.
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