Traffic Speed Deflectometer Measurements Research Articles

Impact of Pavement Stiffness on Performance of Traffic Speed Deflection Measurements

Traffic speed deflection devices (TSDDs) have attracted much attention as innovative tools for assessing the structural capacity of highway networks. However, the sensor and operational limitations of these devices in assessing accurately and precisely the pavement structural condition have not been thoroughly established. In this study, the performance of the traffic speed deflectometer (TSD) measurements was evaluated using data from six instrumented pavement sections. The tested sections included different pavement types such as flexible, composite, and rigid pavements with varying overall level of stiffness. Based on the comparison between TSD measurements and the data obtained from field-installed instrumentation, TSD could measure the deflection velocity profiles reliably at flexible pavement sections. The level of noise superimposed on the signal of TSD deflection velocities was highly dependent on the pavement type and stiffness.

Prediction of critical strains of flexible pavement from traffic speed deflectometer measurements

Structure indicators of flexible pavements can be categorized into material moduli, deflections, distresses, or responses. Since field measurements of pavement responses, such as strains and stresses, require embedded sensors, predictions from easily measured surface deflections are desired. This study aims to predict critical strains of flexible pavements from traffic speed deflectometer (TSD) measurements. Semi-analytical finite element method (SAFEM) was developed and validated for simulation of truck-pavement system by comparison with traditional three-dimensional (3-D) FEM. Results indicated that SAFEM saved significant calculation time while ensuring the same accuracy. The efficient advantage makes it possible to develop an extensive databased of TSD measurement considering a wide rangte of pavement layer thickness, material moduli, temperature, and speed. Traditional regression analysis and artificial neural networks (ANN) models were applied to predict critical tensile, shear, and compressive strains from surface deflection parameters. The developed models were further validated with field measurements. The regression models with explicit forms are easier to understand but require asphalt layer thickness as an input for prediction of tensile or shear strains. Nevertheless, the ANN model offers greater accuracy and only requires the inputs of TSD-measured deflection slopes, which is more applicable for pavement management applications.

Traffic Speed Deflectometer for Network-Level Pavement Management in Tennessee

The traffic speed deflectometer (TSD) overcomes the limitations of the falling weight deflectometer (FWD) with respect to traffic interruption and test inefficiency and is considered the preferred method for network-level pavement structural evaluation. However, some challenges exist when applying the TSD to routine survey work. This study evaluated the long-term repeatability of TSDs and compared TSD measurements with FWD measurements to examine device-related errors in TSD measurements. In addition, a framework for incorporating TSD measurements into the pavement management system (PMS) was developed by introducing deflection bowl parameters (DBPs). All data analyses were based on TSD data collected in Tennessee. From the current study, the repeatability of the TSD has not been fully validated because of device-related errors. Since TSD measurements and FWD measurements are broadly similar, periodic validation of TSDs by FWDs is recommended for quality control. TSDs provide valuable information about pavement structural conditions that are not reliably or adequately reflected in the current PMS (which focuses mainly on pavement functional conditions). DBPs are a simple and fast method for making initial estimates of pavement structural conditions without the need for layer thickness. In the proposed DBP system, the surface curvature index (SCI_12) and D60 (TSD deflection at 60 in. before the load center) represent the structural strength of the asphalt layer and subgrade, respectively. The combination of pavement functional conditions and TSD-measured structural conditions (PMS+DBPs) may help to make more accurate predictions of pavement performance and to make more appropriate decisions on maintenance and rehabilitation strategies.

Reweighted L1 Minimization for Networkwide Weak Spot Detection from Traffic Speed Deflectometer Measurements

The traffic speed deflectometer (TSD) can collect network-level deflection data in a cost-effective and timely manner. However, an automated feature extraction method is needed to interpret such large amounts of data. Basis pursuit (BP) is one such technique: BP can sparsely decompose TSD measurements over a given basis or set of bases of the signal vector space that can represent a particular signal feature. For instance, the TSD surface deflection estimates can be reconstructed as a combination of wavelets, which represent continuously varying features like changes in pavement properties, plus pulses representing the response from structurally weak spots. Yet, the denoised measurements estimated by BP may either be riddled with several false positives (spikes that mismatch real weak spots) or be constructed out of true positive features with damped amplitude. This paper presents reweighed L1 minimization (RWL1), an enhancement to BP to both correct the dampening and discard false positives. This paper introduces RWL1 with examples from simulated data to show its advantage over vanilla BP denoising, plus a demonstration featuring real TSD measurements from a networkwide survey to demonstrate RWL1’s potential as an exploratory analysis tool to detect structurally weak locations within the pavement network worthy of further investigation at the project level.

Estimating Load Transfer Efficiency for Jointed Pavements from TSD Deflection Velocity Measurements

Transverse joints are the weakest element of jointed pavements, and when these joints lack structural capacity, the onset of load-related distress is imminent. The most widespread measurement of the joints’ structural performance is the Load Transfer Efficiency Index (LTE), a ratio of the deflection of the two adjoining slabs. LTE can easily be assessed with a falling weight deflectometer, but this test procedure is not advisable for evaluation at the network level because of user safety concerns and because it can be excessively time-consuming. Traffic speed deflection devices like the traffic speed deflectometer (TSD) are suitable devices for network-level pavement structural evaluation. Yet, as of today, no interpretation technique to get structural health metrics for jointed pavements from TSD data has been published. In this paper, a backcalculation scheme based on slab theory is proposed to estimate the joints’ LTE from TSD deflection velocity measurements. The backcalculation problem formulation and its numerical solution using fast procedures are described in detail. The approach is tested with TSD data collected on the MnROAD test track. Overall, it was found that the backcalculation converges to reasonable estimates of the pavement structural properties and can furnish LTE estimates for most transverse joints from 5 cm-resolution TSD data, all at a reasonable computational cost. This allows for corridor-wide LTE assessment of a pavement’s joints using TSD measurements.

Impact of dynamic loading on pavement deflection measurements from traffic speed deflectometer

Traffic speed deflectometer (TSD) provides an efficient way for evaluating pavement condition. The current principle is to convert measured deflection slopes into the deflection basin using the Area Under The Curve (AUTC) method based on the time-space transform and elastic material theory. This study aims to investigate impact of dynamic loading due to pavement roughness on TSD measurements. Semi-analytical finite element (SAFEM) method was developed to simulate dynamic moving loads on flexible pavements with viscoelastic asphalt layer. The variations of pavement deflection and deflection velocity were found increasing linearly with dynamic loading coefficient (DLC), especially for deflection velocity. The average relative error between the actual deflection basin and the one from the AUTC method could be 15% at DLC of 0.1 with the highest error for the maximum deflection. This indicates the necessity of developing correction method of TSD deflection measurements on rough pavements.

Speed and temperature superposition on traffic speed deflectometer measurements

The traffic speed deflectometer (TSD) is capable of conducting deflection surveys at traffic speeds and is an efficient tool for collecting network-level pavement structural data. Due to the viscoelastic nature of asphalt concrete (AC), the raw TSD deflections need to be corrected to the reference temperature and speed for baseline comparisons with data from different tests. Although the topic of temperature correction for deflections has been extensively discussed, the correction of traffic speed on pavement deflection has rarely been investigated. According to the principle of time–temperature superposition, the time (speed) effect on pavement responses can be evaluated by the concept of “pseudo temperature”. Combined with the test temperature, the time and temperature dependence of AC materials can be integrated into a single factor, referred to as the effective temperature. By correcting raw TSD deflections from the effective temperature to the reference temperature, speed correction and temperature correction can be achieved simultaneously. It also provides some insights into eliminating the need for temperature correction by artificially presetting the TSD speed so that the TSD deflection collected at the preset speed and test temperature is equal to the TSD deflection at the reference speed and reference temperature. Results from the present analysis suggest that TSD deflections are more sensitive to test temperatures than to test speeds. The equivalence between speed and temperature depends on the activation energy of the AC material. For normal TSD operating speeds, a 10 mph increase in test speed is approximately equivalent to a 1 °C drop in temperature. In general, a practical preset speed range of 20–60 mph can accommodate temperature variations of approximately 4 °C.

A new methodology to assess the remaining service life of motorway pavements at the network level from traffic speed deflectometer measurements

ABSTRACT Road agencies are looking for efficient pavement management tools to maintain adequate service levels and preserve the asset value. The traffic speed deflectometer (TSD) allows to continuously assess the structural condition of the pavement while moving at traffic speeds, thus overcoming the typical drawbacks of traditional non-destructive testing equipment. However, managing the huge amount of data deriving from TSD surveys is challenging, and there is no established method to use those data for identifying the remaining service life (RSL) of the pavement network. This study aimed at developing a new practical and reliable methodology to assess the RSL of motorway pavements at the network level from TSD measurements. The methodology is based on the use of the surface curvature index SCI300 and includes the temperature adjustment, the definition of a representative SCI300 value for each pavement section and the estimation of the residual fatigue resistance (considering bottom-up cracking as the critical distress). The methodology was implemented using the deflection data deriving from a TSD survey carried out on about 400 km of motorway pavement. The obtained RSL predictions can be used to identify the structural ranking of different pavement sections and guide budget forecasting, according to the actual maintenance and rehabilitation priorities.

Ten Years of Traffic Speed Deflectometer Research in the United States: A Review

This paper summarizes the research efforts into and applications of the traffic speed deflectometer (TSD) that have occurred over the last 10 years in the United States (U.S.). These are divided into three main topics which are: (1) data analysis methods including data processing, calculation of indices and structural parameters, and temperature correction; (2) evaluation efforts of TSD including repeatability and comparison with the falling weight deflectometer (FWD); and (3) applications of TSD with a focus on flexible pavement management applications, although some efforts on rigid and composite pavements are mentioned. The data analysis methods reviewed are data averaging length and reporting interval, and how deflections are calculated from the measured deflection slopes. For the calculation of indices and structural parameters the following are reviewed: deflection indices that have been proposed, the effective structural number ( SNeff), and backcalculation of layer moduli. Efforts to evaluate TSD precision (or measurement standard error) are discussed. Then, some comparisons of short-term (time frame of 1 day) and long-term (time frame of 1 year) repeatability of TSD are presented. Compared with FWD, TSD and FWD generally have good correlation, although the measurement principles of the two devices are slightly different. The focus is on network-level applications of TSD, and, more specifically, efforts to include TSD measurements in the pavement management system, especially for flexible pavements applications.

A parameter identification technique for traffic speed deflectometer tests of pavements

The structural evaluation of existing pavements forms the basis for formulating cost-effective maintenance and rehabilitation strategies. A promising tool for pavement structural evaluation at network level is the Traffic Speed Deflectometer (TSD) test. However, the application of the TSD test is hindered by the lack of a robust and efficient parameter identification technique. To solve this problem, a theoretical model for the TSD test is first formulated. Then, a minimisation algorithm which works best with the theoretical TSD model for parameter identification is selected. Finally, the performance of this combination in processing field TSD measurements is studied. The results show that the modified Levenberg-Marquardt algorithm using all the 9 detection points is most suitable to be combined with the theoretical TSD model for parameter identification, which gives a promising parameter identification technique for TSD tests of pavements. The presented work contributes to the development of technologies for pavement structural evaluation.

Determining pavement structural number with traffic speed deflectometer measurements

Traffic Speed Deflectometer (TSD) overcomes the slow operating speed of the conventional Falling Weight Deflectometer (FWD), but it also introduces new difficulties in estimating the effective structural number (SN) of existing pavements. Due to the different loading mechanisms of TSD and FWD, the FWD-based SN calculation procedure proposed in the AASHTO Pavement Design Guide is incompatible with TSD measurements. To match the existing FWD-based SN records, the authors proposed a new approach to determine the SN with TSD measurements by modifying the AASHTO method. Simulation results show that the difference in AASHTO SN between TSD and FWD essentially came from the difference in the equivalent loading frequency. The asphalt concrete (AC) layer exhibited lower stiffness under TSD than under FWD. The TSD-based AASHTO SN was generally smaller than the FWD-based AASHTO SN (error of 0.74), and the deviation increased with increasing AC thickness. Therefore, an enhanced AASHTO method was proposed for calibrating TSD-based SN by the AC thickness, which made great progress in estimating SN from TSD deflections with an error of 0.17. The proposed method was verified by filed TSD and FWD measurements and was recommended for pavements with AC thickness greater than 4 in..

Quality control of Traffic Speed Deflectometer measurements on road network

Nośność konstrukcji nawierzchni drogowych jest traktowana jako istotny element planowania utrzymania podczas procesu decyzyjnego na poziomie sieci. Biorąc pod uwagę ugięcia jako bezpośredni wskaźnik nośności nawierzchni, można precyzyjnie i skutecznie ocenić zarówno całą sieć drogową, jak i poszczególne jej odcinki. Do prawidłowej oceny tego parametru niezbędna jest efektywna metoda pomiarowa oraz zapewnienie możliwie najwyższej jakości danych. Wykonanie pomiaru ugięć na poziomie sieci jest możliwe dzięki zastosowaniu ugięciomierza Traffic Speed Deflectometer (TSD), który jest najnowocześniejszym urządzeniem diagnostycznym do badania nośności nawierzchni drogowych. Utrzymanie jakości danych uzyskiwanych na podstawie badań tym urządzeniem powinno następować poprzez wdrażanie odpowiednich procedur kontrolnych. W artykule przedstawiono jedną z najczęściej stosowanych metod kontroli jakości pomiarów ugięć z wykorzystaniem ugięciomierza TSD oraz związany z tą metodą problem oceny wyników kontroli w kontekście różnic rejestrowanych wartości temperatur nawierzchni asfaltowych i wskaźnika równości nawierzchni. W przeprowadzonej analizie danych wykazano, że średnia różnica temperatury między pomiarem rutynowym a kontrolnym większa niż 10°C może powodować istotną różnicę uzyskanego wyniku. Jednocześnie wykazano, że porównywanie wskaźników krzywizny powierzchni SCI300 na etapie kontroli własnej jest obarczone mniejszymi błędami niż porównania ugięć maksymalnych D0. Ocenie poddano również nierówności nawierzchni, które negatywnie wpływały na wynik kontroli własnej.

Development of an Artificial Neural Network-Based Procedure for the Verification of Traffic Speed Deflectometer Measurements

Since the 1980s, the falling weight deflectometer (FWD) has been the primary deflection-measuring device in the United States to evaluate the structural conditions of in-service pavements. However, the stop and go nature of the FWD limits its application at the network level. In the early 2000s, the traffic speed deflectometer (TSD) was introduced as an alternate deflection-measuring device for network-level applications. TSD collects deflection measurements while traveling at traffic speed, which provides improved spatial coverage and no traffic disturbance. The verification of TSD measurements is of great interest as many agencies move toward widespread implementation. This study aims at developing a reliable and straightforward procedure for the verification of TSD measurements using limited FWD measured deflection measurements. The verification procedure employs a trained artificial neural network (ANN) model to shift TSD deflections to their corresponding FWD deflections. The ANN model was trained and verified based on FWD and TSD measurements from two deflection-testing programs. The developed model accurately predicted FWD measurements with a coefficient of determination (R2) of 0.994. The suitability of the proposed verification procedure was evaluated using statistical and engineering-based measures and showed acceptable accuracy. Results also validated that the proposed method could be used to verify TSD measurements before its use for conducting deflection measurements at the network level.

Utilization of traffic speed deflectometer for pavement structural evaluations

Traffic Speed Deflectometer (TSD) is primarily capable of evaluating the pavement conditions at the network level with regard to the structural capacities. This paper mainly reviews the utilization of TSD for the pavement structural evaluations. With the Doppler lasers configured in a specific truck, TSD can measure the deflection velocity (slope) at a traffic speed. The measured deflection can be calculated by either area under the curve (AUTC) method or Euler–Bernoulli beam (EB) method. Moreover, the pavement moduli backcalculated by either 3D Move simulation or the Artificial Neural Networks (ANNs) from TSD measurements tend to well correlate those backcalculated from FWD measurements. It is noted that structural capacities derived from TSD measurement were reliable in assessing the pavement conditions. Additionally, the pavement roughness has a significant effect on TSD measurements. Therefore, Traffic Speed Deflectometer has a promising applied prospect in pavement structural evaluations.

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

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.

A nonlinear spectral element model for the simulation of traffic speed deflectometer tests of asphalt pavements

ABSTRACT A non-destructive testing method suitable for network-level pavement structural evaluation is the traffic speed deflectometer (TSD) test. However, the analysis of TSD measurements still needs a proper parameter back-calculation procedure, which requires an accurate and efficient forward calculation model. As a first step to solving this issue, a nonlinear spectral element model which can simulate TSD tests of asphalt pavements is developed. The model is used to investigate the characteristics and parameter sensitivity of the response of asphalt pavements caused by the TSD loading. The results indicate that the vertical deflection curve along the direction of movement observed on the asphalt pavement surface is slightly asymmetric, and the maximum deflection appears behind the centre of the loading area. In addition, the slope curve of vertical deflection is highly sensitive to the magnitude of the applied force, the moduli of the base layer and subgrade, and the thicknesses of the asphalt layer and base layer. Furthermore, the slope curve is relatively sensitive to the glassy modulus of the asphalt layer. Because of its good predictive capability and high computational efficiency, the proposed model has the desired characteristics to be used as the computational kernel for parameter back-calculation procedures of TSD measurements.

Mechanistic-Based Approach to Utilize Traffic Speed Deflectometer Measurements in Backcalculation Analysis

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) deflection measurements; however, the stationary nature of the FWD requires lane closure and traffic control. In recent years, traffic speed deflection devices such as the traffic speed deflectometer (TSD), which can continuously measure pavement surface deflections at traffic speed, have been introduced. In this study, a mechanistic-based approach was developed to convert TSD deflection measurements into the equivalent FWD deflections. The proposed approach uses 3D-Move software to calculate the theoretical deflection bowls corresponding to FWD and TSD loading configurations. Since 3D-Move requires the definition of the constitutive behaviors of the pavement layers, cores were extracted from 13 sections in Louisiana and were tested in the laboratory to estimate the dynamic complex modulus of asphalt concrete. The 3D-Move generated deflection bowls were validated with field TSD and FWD data with acceptable accuracy. A parametric study was then conducted using the validated 3D-Move model; the parametric study consisted of simulating pavement designs with varying thicknesses and material properties and their corresponding FWD and TSD surface deflections were calculated. The results obtained from the parametric study were then incorporated into a Windows-based software application, which uses artificial neural network as the regression algorithm to convert TSD deflections to their corresponding FWD deflections. This conversion would allow backcalculation of layer moduli using TSD-measured deflections, as equivalent FWD deflections can be used with readily available tools to backcalculate the layer moduli.

Visco-Elastic Back-Calculation of Traffic Speed Deflectometer Measurements

The traffic speed deflectometer (TSD) has proven a valuable tool for network level structural evaluation. At the project level, however, the use of TSD data is still quite limited. An obstacle to the use of TSD at the project level is that the standard approaches to back-calculation of pavement properties are based on the falling weight deflectometer (FWD). The FWD experiment is similar, but not equivalent, to the TSD experiment, and therefore it is not straightforward to apply the traditional FWD back-calculation procedures to TSD data. In this paper, a TSD-specific back-calculation procedure is presented. The procedure is based on a layered linear visco-elastic pavement model and takes the driving speed of the vehicle into account. This is in contrast to most existing back-calculation procedures, which treat the problem as static and the pavement as purely elastic. The developed back-calculation procedure is tested on both simulated and real TSD data. The real TSD measurements exhibit significant effects of damping and visco-elasticity. The back-calculation algorithm is able to capture these effects and yields model fits in excellent agreement with the measured values.

Temperature adjustment of Surface Curvature Index from Traffic Speed Deflectometer measurements

ABSTRACT Pavement structural condition evaluation plays an important role in assessing rehabilitation needs and making prudent investment decisions. Recently the use of Traffic Speed Deflectometer (TSD), is being considered for pavement structural evaluation especially for network level pavement management applications. Among various TSD data measurements and indices, Surface Curvature Index (SCITSD), is a widely-used index for assessing the structural condition of the AC layers. Since asphalt modulus is temperature sensitive, measured deflections and consequently the computed SCITSD are also affected by pavement temperature at the time of testing. For consistent pavement evaluation, SCITSD computed across sections and over time should be adjusted to a reference temperature. This study presents an applied method for adjusting SCITSD to a reference temperature. The method was based on a model developed from a dataset built from responses computed with dynamic-viscoelastic analyses under TSD moving load in a wide range of pavement properties. The method was field evaluated with TSD data from three locations within the US and one location each in Europe and Australia. The results showed that the proposed method can reasonably adjust SCITSD to a reference temperature which enables its application in pavement structural assessment and subsequent use in pavement management activities.

Comparing Traffic Speed Deflectometer and Falling Weight Deflectometer Data

In recent years the pavement engineering community has shown increasing interest in shifting from a stationary falling weight deflectometer (FWD) to moving testing platforms such as the traffic speed deflectometer (TSD). This paper dealt with comparing TSD measurements against FWD measurements; it focused on the comparison methodology, utilizing experimental data for demonstration. To better account for differences in loading conditions between the two devices a new FWD deflection index was formulated first. This index served as reference/benchmark for assessing the corresponding TSD measurements. Next, a Taylor diagram was proposed for visualizing several comparison statistics. Finally, a modern agreement metric was identified and applied for ranking comparison results across different datasets. Overall, the suggested methodology is deemed generic and highly applicable to future situations, especially for assessing the worth of emerging device upgrades or improved interpretation schemes (or both).