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Non-contact Surface Wave Testing Inversion by Artificial Neural Networks

The advantage of non-contact, air-coupled sensing is that it provides an opportunity for a quick scanning and imaging of large civil engineering structures. The non-contact Spectral Analysis of Surface Waves SASW test is considered to be an extension of the traditional SASW test, where the ground-coupled sensors are replaced by non- contact sensors to collect the leaky surface waves, instead of ground vibration. The objective of the test is to determine the leaky Rayleigh wave dispersion curve, i.e. the velocity of leaky Rayleigh waves as a function of frequency, and then through a process of inversion to estimate the elastic modulus profile. Significant efforts have been devoted to develop a general automated inversion procedure. This paper presents the development and application of Artificial Neural Networks (ANNs) for an automated inversion of non-contact SASW test data. Numerical simulation of the air-coupled SASW test on several hundred pavement configurations using finite elements was made to develop an extensive database of surface wave dispersion curves. The database was used in the development of the ANN for an automated inversion. The developed automated system was validated through inversion of additional data not used in training of the ANN (i.e. synthetic dispersion curves), and it showed a good performance in prediction of pavement profiles.




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