The response data from pavement sensors are routinely collected in order to study the stress state of the pavement. The differences, which refer to corresponding peaks and troughs generated when the truck wheels pass the strain sensors, provide scientific data for the pavement structure design in order to extend the pavement service period. However, manually identifying the peaks and troughs of the large-scaling sensor signals is laborious and imprecise. In this paper, the effects of low-pass filtering and wavelet filtering are analyzed on the load signals. More importantly, the adaptive maximum energy ratio method and standard deviation method are proposed to detect the load signal areas to reduce the noise interference. Finally, the program to process batches of signals automatically, is developed to find the most influential strain more accurately in comparison with conventional methods, which is instructive for pavement structure design and preservation.

CICTP