Visco-Elastic Back-Calculation of Traffic Speed Deflectometer Measurements

2019 ◽  
Vol 2673 (12) ◽  
pp. 439-448 ◽  
Author(s):  
Christoffer P. Nielsen
Keyword(s):  
Calculation Procedure ◽  
Falling Weight Deflectometer ◽  
Calculation Algorithm ◽  
Structural Evaluation ◽  
Driving Speed ◽  
Back Calculation ◽  
Traffic Speed ◽  
Falling Weight ◽  
Calculation Procedures ◽  
Project Level

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.

Application of Traffic Speed Deflectometer for Network-Level Pavement Management

2018 ◽  
Vol 2672 (40) ◽  
pp. 348-359 ◽  
Author(s):  
Shivesh Shrestha ◽  
Samer W. Katicha ◽  
Gerardo W. Flintsch ◽  
Senthilmurugan Thyagarajan
Keyword(s):  
Surface Condition ◽  
Structural Condition ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Pavement Management System ◽  
State Highway ◽  
Structural Evaluation ◽  
Traffic Speed ◽  
State Highway Agencies ◽  
Falling Weight

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.

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

2018 ◽  
Vol 45 (5) ◽  
pp. 377-385 ◽  
Author(s):  
Omar Elbagalati ◽  
Momen Mousa ◽  
Mostafa A. Elseifi ◽  
Kevin Gaspard ◽  
Zhongjie Zhang
Keyword(s):  
Traffic Control ◽  
Coefficient Of Determination ◽  
Falling Weight Deflectometer ◽  
Ann Model ◽  
Acceptable Accuracy ◽  
Traffic Speed ◽  
Artificial Neural Network Ann ◽  
Falling Weight ◽  
Good Agreement

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.

scholarly journals Comparing Traffic Speed Deflectometer and Falling Weight Deflectometer Data

2018 ◽  
Vol 2672 (40) ◽  
pp. 22-31 ◽  
Author(s):  
Eyal Levenberg ◽  
Matteo Pettinari ◽  
Susanne Baltzer ◽  
Britt Marie Lekven Christensen
Keyword(s):  
Experimental Data ◽  
Loading Conditions ◽  
Falling Weight Deflectometer ◽  
Engineering Community ◽  
Taylor Diagram ◽  
Comparison Results ◽  
Pavement Engineering ◽  
Traffic Speed ◽  
Falling Weight

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).

Direct Method for Evaluating Structural Needs of Flexible Pavements with Falling-Weight Deflectometer Deflections

2003 ◽  
Vol 1860 (1) ◽  
pp. 41-47 ◽  
Author(s):  
Mario S. Hoffman
Keyword(s):  
Direct Method ◽  
Flexible Pavements ◽  
Falling Weight Deflectometer ◽  
Simple Method ◽  
Traffic Demand ◽  
Structural Evaluation ◽  
Overlay Design ◽  
Subgrade Modulus ◽  
Simple Equations ◽  
Falling Weight

A direct and simple method (YONAPAVE) for evaluating the structural needs of flexible pavements is presented. It is based on interpretation of measured falling-weight deflectometer (FWD) deflection basins using mechanistic and practical approaches. YONAPAVE estimates the effective structural number (SN) and the equivalent subgrade modulus independently of the pavement or layer thicknesses. Thus, there is no need to perform boreholes, which are expensive, time-consuming, and disruptive to traffic. Knowledge of the effective SN and the subgrade modulus together with an estimate of the traffic demand allows the determination of the overlay required to accommodate future needs. YONAPAVE’s simple equations can be solved using a pocket calculator, making it suitable for rapid estimates in the field. The simplicity of the method, and its independence from major computer programs, make YONAPAVE suitable for estimating the structural needs of a road network using FWD data collected on a routine or periodic basis along network roads. YONAPAVE can be used with increased experience and confidence as the basis for nondestructive testing structural evaluation and overlay design at the project level.

A practical approach to falling-weight deflectometer (FWD) data reduction

2005 ◽  
Vol 42 (2) ◽  
pp. 641-645 ◽  
Author(s):  
Dieter Stolle ◽  
Peijun Guo
Keyword(s):  
Mechanical Properties ◽  
Shear Modulus ◽  
Falling Weight Deflectometer ◽  
Layer System ◽  
Surface Loading ◽  
Back Calculation ◽  
Pavement Response ◽  
Subgrade Modulus ◽  
Falling Weight

The authors present a simplified methodology for preprocessing falling-weight deflectometer (FWD) data, which identify a pseudo-static pavement response to surface loading. This allows one to employ static analysis to back-calculate the mechanical properties of the pavement–subgrade system. It is shown that the subgrade modulus can be identified, independent of the details of the pavement structure itself, at least for a two-layer system. The quality of the effective shear modulus is sensitive to the value of Poisson's ratio selected.Key words: pavement–subgrade system, subgrade modulus, back-calculation, FWD.

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

2020 ◽  
Vol 2674 (5) ◽  
pp. 208-222
Author(s):  
Zia U. A. Zihan ◽  
Mostafa A. Elseifi ◽  
Patrick Icenogle ◽  
Kevin Gaspard ◽  
Zhongjie Zhang
Keyword(s):  
Asphalt Concrete ◽  
Traffic Control ◽  
Parametric Study ◽  
Complex Modulus ◽  
Falling Weight Deflectometer ◽  
Software Application ◽  
Traffic Speed ◽  
Definition Of ◽  
Falling Weight ◽  
Surface Deflections

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.

scholarly journals Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

2020 ◽  
Author(s):  
Nader Karballaeezadeh ◽  
Hosein Ghasemzadeh Tehrani ◽  
Danial Mohammadzadeh S. ◽  
Shahaboddin Shamshirband
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ground Penetrating Radar ◽  
Flexible Pavement ◽  
Falling Weight Deflectometer ◽  
Machine Learning Methods ◽  
Back Calculation ◽  
Ground Penetrating ◽  
Falling Weight ◽  
Surface Deflections

The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

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