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A platform for research: civil engineering, architecture and urbanism
ANN Backcalculation of Pavement Profiles from the SASW Test
The SASW test is a seismic technique for in situ evaluation of elastic moduli and layer thicknesses for layered systems, such as pavements. The objective of the SASW test is to obtain the experimental dispersion curve, and then through an inversion or backcalculation procedure to obtain the elastic modulus profile. The inversion process is a complex process that requires a significant computational effort and often requires operator's intervention. An artificial neural network was developed, that backcalculates accurately in real time pavement profiles from the SASW test collected data. The network is developed using synthetic dispersion curves from a numerical simulation of the SASW test. The most important feature of the developed network is that it consists of several neural network models used in evaluation of the thickness and elastic modulus of each individual layer. This results in a significant improvement in accuracy of evaluation of pavement properties. In comparison to the previous ANN models, accuracy can be attributed to the individual layer approach and to the use of a data transformation algorithm. The stiffness matrix approach used in generation of synthetic dispersion curves is described.
ANN Backcalculation of Pavement Profiles from the SASW Test
The SASW test is a seismic technique for in situ evaluation of elastic moduli and layer thicknesses for layered systems, such as pavements. The objective of the SASW test is to obtain the experimental dispersion curve, and then through an inversion or backcalculation procedure to obtain the elastic modulus profile. The inversion process is a complex process that requires a significant computational effort and often requires operator's intervention. An artificial neural network was developed, that backcalculates accurately in real time pavement profiles from the SASW test collected data. The network is developed using synthetic dispersion curves from a numerical simulation of the SASW test. The most important feature of the developed network is that it consists of several neural network models used in evaluation of the thickness and elastic modulus of each individual layer. This results in a significant improvement in accuracy of evaluation of pavement properties. In comparison to the previous ANN models, accuracy can be attributed to the individual layer approach and to the use of a data transformation algorithm. The stiffness matrix approach used in generation of synthetic dispersion curves is described.
ANN Backcalculation of Pavement Profiles from the SASW Test
Gucunski, N. (author) / Abdallah, I. N. (author) / Nazarian, S. (author)
Geo-Denver 2000 ; 2000 ; Denver, Colorado, United States
2000-07-24
Conference paper
Electronic Resource
English
ANN Backcalculation of Pavement Profiles from the SASW Test
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