Traffic Speed Deflectometer Measurements Research Articles

Development of a methodology to backcalculate pavement layer moduli using the traffic speed deflectometer

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) measurements; however, the stationary nature of FWD requires lane closure and traffic control. To overcome these limitations, a number of continuous deflection devices were introduced in recent years. The objective of this study was to develop a methodology to incorporate traffic speed deflectometer (TSD) measurements in the backcalculation analysis. To achieve this objective, TSD and FWD measurements were used to train and to validate an artificial neural network (ANN) model that would convert TSD deflection measurements to FWD deflection measurements. The ANN model showed acceptable accuracy with a coefficient of determination of 0.81 and a good agreement between the backcalculated moduli from FWD and TSD measurements. Evaluation of the model showed that the backcalculated layer moduli from TSD could be used in pavement analysis and in structural health monitoring with a reasonable level of accuracy.

Development of a Structural Capacity Prediction Model Based on Traffic Speed Deflectometer Measurements

The importance of incorporating pavement structural conditions in the selection of maintenance and rehabilitation strategies along with functional indices has been recognized by state agencies. To measure in-service pavement structural capacity, surface deflection under a defined load has been typically used. The traffic speed deflectometer (TSD) has emerged as a continuous deflection-measuring device as it operates at traffic speed and reduces lane closure and user delays. The present study developed a nonlinear regression model to predict pavement structural number (SN) based on surface deflections measured by the TSD along with the total pavement layer thickness and traffic volume. The proposed model was successfully developed and validated with SN calculated based on TSD and falling weight deflectometer deflection data obtained from two testing programs in Louisiana and Idaho. The model was further validated with respect to its prediction of in-service structural capacity loss and deficiency. Based on statistical measures and the model’s ability in identifying structurally deficient sections, results showed satisfactory accuracy of the model and supports its use for network-level decision-making processes in the pavement management system.

Application of Traffic Speed Deflectometer for Network-Level Pavement Management

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.

Adaptive False Discovery Rate for Wavelet Denoising of Pavement Continuous Deflection Measurements

AbstractThe traffic speed deflectometer (TSD) is a device used to evaluate the pavement’s structural condition. Measurements obtained from the TSD are affected by noise, which can make it hard to interpret test results. The main objective of this paper is to develop a denoising methodology to use with TSD measurements and improve pavement structural evaluation. The denoising methodology comprises a computational algorithm to identify significant features in a high-dimensional vector of observations containing white Gaussian noise. The algorithm minimizes the classification error of features in the wavelet transform domain by adaptively selecting the level at which to control the false discovery rate. When tested in a simulation study, the results of the proposed algorithm compared favorably with other state-of-the-art methods. The proposed methodology was then successfully used with TSD measurements to identify possible weak joints in a jointed concrete pavement overlaid with an asphalt layer and to calcu...

Estimating “True” Variability of Traffic Speed Deflectometer Deflection Slope Measurements

AbstractIn this paper, the difference sequence method is used to decompose traffic speed deflectometer (TSD) deflection slope measurements variability into “true” spatial variability that is due to pavement structural changes and noise variability. A robust method to evaluate the noise standard deviation is presented and the difference sequence method is validated using simulated examples. The difference sequence method is then used to calculate the noise standard deviation and “true” spatial variability of TSD deflection slope measurements. The evaluated noise standard deviation is also used to determine the optimal smoothing of TSD deflection slope measurements using an unbiased measure of the risk. This method is compared with the generalized cross validation (GCV) criterion to determine the optimal smoothing. Results suggest the unbiased risk criterion performs better than the GCV criterion. This gives an objective method, which is currently lacking, to average TSD measurements, and more generally any...

Limits of agreement method for comparing TSD and FWD measurements

This article uses the limits of agreement (LOA) method to compare the falling weight deflectometer (FWD) and the traffic speed deflectometer (TSD), two pavement structural evaluation devices. The TSD measures deflection slope, whereas the FWD measures deflection. For this reason, measurements were converted to the surface curvature index (SCI) and the base damage index (BDI), which can be obtained from each device. The SCI and BDI agreement between the two devices was then evaluated. Although the relationship between the calculated SCI and BDI using both equipments is reasonably close to the line of equality, there is a significant variation and a bias in this relationship. For example, for an average SCI or BDI value of 300 μm, the bias was 30 μm (FWD values lower than TSD values), and the LOA was 380 μm.

Optimal Averaging and Localized Weak Spot Identification of Traffic Speed Deflectometer Measurements

One of the main difficulties that arise during the analysis of continuous deflection measurements is the reduction of measurement variability (noise) without smoothing out important features or adversely affecting the true spatial variability, and no objective method has been available. The use of the smoothing spline determined through minimization of the generalized cross-validation criteria is proposed for denoising traffic speed deflectometer (TSD) deflection slope measurements. The generalized cross-validation criteria find the optimal trade-off between variance and bias so that the estimated smoothing spline gives the best smoothing fit (weighted moving average fit) for minimum mean square error with respect to the true deflection slope values. The smoothing spline fit allows the construction of a confidence interval for the true deflection slope as well as the use of outlier detection methods for identifying localized weak spots, such as those caused by cracks or weak joints in jointed concrete sections. In each case, the distribution and standard deviation of the measurement error is needed. It is shown that TSD measurement error distribution is well represented by a normal distribution, and an outlier robust method is used to evaluate the standard deviation. Example data analysis uses computer-generated data to simulate a smoothly varying deflection slope profile as well as a homogeneous profile with localized weak spots and uses actual TSD deflection slope measurements. The results show that smoothing spline denoising can significantly improve the interpretation of TSD deflection slope measurements.