Identifying Weak Joints in Jointed Concrete and Composite Pavements from Traffic Speed Deflectometer Measurements by Basis Pursuit

Abstract

Weak joints are the source of most performance problems in jointed concrete pavements (JPCPs) and composite pavements created by overlaying a JPCP. Until recently, it has been prohibitively time-consuming to evaluate the joints in a road network, and the evaluation of joint load transfer efficiency (LTE) has been restricted to specific project-level applications. This is changing with the advent of the traffic speed deflectometer (TSD), a device that can collect structural information data at a 1-m resolution while moving at traffic speed. The 1-m resolution is adequate to capture the response of the joints to the applied load, but it results in a higher noise level compared with data collected at lower resolutions (e.g., at the typical 10 m). This paper proposes the use of basis pursuit (BP) denoising to extract meaningful information about the joints’ condition from the (noisy) TSD measurements. Weak joints are modeled as spikes (Dirac basis) in the measurements, and the remaining features in the measurements are modeled on a wavelet basis. Combining the two bases (Dirac basis and wavelet basis) results in multiple possible representations of the collected measurements; essentially there are twice as many unknowns as equations to determine these unknowns. BP denoising seeks a representation with a small number of elements from the two bases. Because weak joints are represented best by spikes, BP denoising results mostly in selecting the spikes at the weak joint locations. These identified spikes provide a list of weak joints that can be used to prioritize available resources (e.g., which joints should be investigated further or fixed first). We present examples of BP denoising using simulated data and actual TSD collected data.