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Continuous deflection devices (CDDs) can safely measure pavement deflection (or other related properties) while traveling at highway speed, which reduces traffic disruption. CDD measurements are contaminated with relatively high noise levels compared to stop‐and‐go devices such as the Falling Weight Deflectometer. In this article, we use wavelet transform denoising to remove the noise and estimate the true deflection slope measurements obtained from the Traffic Speed Deflectometer. Results show that failure to denoise deflection slope measurements can lead to calculated Effective Structural Number values that are highly variable (unstable). Attempting to filter these highly variable measurements can lead to erroneous results. We also use wavelet transform denoising to identify localized weak spots such as those that are caused by pavement reflection cracking. Identifying weak spots with wavelets is possible because wavelets are spatially adaptive to local features. In contrast, a linear filter is not capable of adapting to local features.

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Continuous deflection-measuring devices, or continuous deflectometers, are increasingly being used to support project-level and network-level pavement management decisions. Continuous deflectometers are nondestructive pavement evaluation devices that measure pavement deflections caused by a moving load. Some continuous deflectometers can measure with little to no traffic control; this feature makes them more advantageous to use than stationary devices, such as the falling weight deflectometer. The current technologies implemented in different types of deflectometers are discussed, and the most promising devices for supporting network-level pavement management decisions are identified. In that respect, two devices (the rolling wheel deflectometer and the traffic speed deflectometer) have shown promising results and are being evaluated further under Project R06 (F) of SHRP 2.

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Modelling the pavement deterioration process is essential for a successful pavement management system (PMS). The pavement deterioration process is highly influenced by uncertainties related to data acquisition and condition assessment. This paper presents a novel approach for predicting a pavement deterioration index. The model builds on a negative binomial (NB) regression used to predict pavement deterioration as a function of the pavement age. Network-level pavement condition models were developed for interstate, primary, and secondary pavement road families and were compared with traditional non-linear regression models. The linear empirical Bayesian (LEB) approach was then used to improve the predictions by combining the deterioration estimated by the fitted model and the observed/measured condition recorded in the PMS. The proposed approach can improve the mean square error prediction of the next-year pavement condition by 33%, 36% and 41% for Interstate, Primary, and Secondary roads, respectively, compared with the measured pavement condition without further modelling of the pavement deterioration.2

In this paper, the traffic speed deflectometer (TSD), a device used for network level structural evaluation, is assessed. TSD testing was performed in nine states on a total of 5,928 miles (some repeated) during three time periods: November 2013, May to July 2014, and June to September 2015. This paper presents (1) the results of repeatability and comparison of the TSD with the falling weight deflectometer (FWD), (2) the results of the comparison of TSD measurements with typical pavement management system (PMS) data, and (3) an approach that can be implemented by State Highway Agencies (SHAs) to incorporate indices derived from TSD data into their PMS decision-making process. The results show that repeated TSD measurements follow similar trends and the TSD measurements and FWD measurements on the same pavement sections follow similar trends as well. Comparing TSD measurements with PMS surface condition data confirmed that the TSD provided valuable information about the structural condition of the tested pavement sections that cannot be derived from the already available pavement surface condition as part of an agency’s PMS. An example of how TSD information can be used to refine the triggered maintenance treatment category as part of a network-level PMS analysis is presented for a roughly 75-mile section of I-81 south in Virginia.