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.

Modulus Back-Calculation Based on FWD Dynamic Deflection Basin for Semi-Rigid Base Asphalt Pavement

2014 ◽  
Vol 587-589 ◽  
pp. 1062-1066
Author(s):  
Chuan Yi Zhuang ◽  
Ya Li Ye
Keyword(s):  
Fly Ash ◽  
Asphalt Pavement ◽  
Rigid Base ◽  
Falling Weight Deflectometer ◽  
Loading Test ◽  
Response Characteristics ◽  
Rate Of Decay ◽  
Back Calculation ◽  
Pavement Response ◽  
Falling Weight

To study semi-rigid base asphalt pavement response characteristics, two structural APT test roads of cement stabilized aggregate and lime fly-ash stabilized aggregate were constructed, and accelerated loading test was developed. After certain number of wheel loads (70,000 times) pavement deflection basin parameters were detected by using the falling weight deflectometer, then modulus of each pavement layer was back-calculated, dynamic response was analyzed. Studies have shown that the modulus of semi-rigid base decay faster, and with decreasing thickness of the semi-rigid base, the greater the rate of decay of modulus; cement stabilized aggregate decay rate is greater than that of lime fly-ash stabilized aggregate.

Influence of Cracking Damage on Deflection Basin Test Data of FWD

2014 ◽  
Vol 620 ◽  
pp. 55-60 ◽  
Author(s):  
Xin Qiu ◽  
Xiao Hua Luo ◽  
Qing Yang
Keyword(s):  
Probabilistic Approach ◽  
Asphalt Pavements ◽  
Falling Weight Deflectometer ◽  
Layer System ◽  
Linear Elastic ◽  
Distribution Features ◽  
Pavement Structures ◽  
And Performance ◽  
Falling Weight ◽  
Non Destructive

With the popularization of falling weight deflectometer (FWD) to calculate the stiffness related parameters of the pavement structures, non-destructive evaluation of physical properties and performance of pavements has taken a new direction. FWD backcalculation is mathematically an inverse problem that could be solved either by deterministic or by probabilistic approach. A review of the currently used backcalculation procedures indicates that the calculation is generally based on a homogeneous, continuous, and linear elastic multi-layer system. Identifying effective data of dynamic deflection basins seems to be an important task for performing modulus backcalculation. Therefore, the main objective of this paper was to discuss the distribution features of dynamic deflection basins of asphalt pavements with crack distresses, and present the reasonable criteria to filter the testing data of FWD deflection basins. Finally, the study aims to validate the established criteria by conducting in-situ case study.

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.

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.

Interpretation of pavement deflection measurement data using an elastodynamic stochastic approach

1998 ◽  
Vol 25 (1) ◽  
pp. 151-160 ◽  
Author(s):  
Mehdi Parvini ◽  
Dieter FE Stolle
Keyword(s):  
Finite Element ◽  
Measurement Data ◽  
Stochastic Approach ◽  
Falling Weight Deflectometer ◽  
Stochastic Finite Element ◽  
Element Approach ◽  
Subgrade Modulus ◽  
Low Sensitivity ◽  
Falling Weight ◽  
Surface Deflections

Pavement deflection measurements, together with backcalculation procedures, are widely used to estimate the layer moduli of pavement-subgrade systems. Sensitivity analysis of a sample problem indicates that conclusions drawn from static analyses with regards to deflection sensitivity to variation in layer moduli may apply when characterizing uncertainty associated with the interpretation of the falling weight deflectometer (FWD) data. The uncertainty associated with the values of the backcalculated parameters from deflection data is investigated in this paper using an elastodynamic, stochastic finite element approach. The results of the simulations indicate that, in order to properly estimate surface layer moduli, loading frequencies higher than that of excitation by typical FWD loading are required. The low sensitivity of deflection uncertainty to random variations in surface modulus, when compared with that associated with subgrade modulus, is demonstrated to contribute to high variations in backcalculated surface modulus from measured surface deflections. Although focus is placed on uncertainties in elastic modulus and deflection, the methodology presented in the paper can be used to quantify uncertainties associated with other layer properties and pavement responses.Key words: stochastic, finite element, pavement deflection, elastodynamic, backcalculation, layer moduli, falling weight deflectometer test.

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.

Static and Dynamic Backcalculation Analyses of an Inverted Pavement Structure

2013 ◽  
Vol 723 ◽  
pp. 196-203 ◽  
Author(s):  
James Maina ◽  
Wynand JvdM Steyn ◽  
Emile B. van Wyk ◽  
Frans le Roux
Keyword(s):  
South African ◽  
Maintenance Strategy ◽  
Falling Weight Deflectometer ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Back Calculation ◽  
The Difference ◽  
Falling Weight ◽  
Non Destructive ◽  
Crucial Part

A crucial part of any maintenance strategy is an intricate understanding of the material characteristics of the pavement, so that the current level of damage may be accurately assessed and an appropriate plan implemented. Advances in the precision to which these parameters can be determined, as well as improvements in how these results are interpreted under varying conditions of measurement and analysis, are essential in the effective execution of a maintenance strategy. Results from Falling Weight Deflectometer (FWD), which is a Non-Destructive Testing (NDT) device, can be used to predict elastic modulus of any layer by comparing measured deflection data to calculated values through an iterative process referred to as back-calculation. This paper presents a comparison between static and dynamic back-calculation procedures, specifically with regard to typical South African inverted pavements. The analysis indicates a dynamic analysis provides results of greater accuracy than a static analysis, although the effect of the difference requires further investigation.

Prediction of Remaining Life of Asphalt Pavement with Falling-Weight Deflectometer Multiload-Level Deflections

2003 ◽  
Vol 1860 (1) ◽  
pp. 48-56 ◽  
Author(s):  
Hee Mun Park ◽  
Y. Richard Kim
Keyword(s):  
Flexible Pavements ◽  
Fatigue Cracking ◽  
Pavement Performance ◽  
Falling Weight Deflectometer ◽  
Remaining Life ◽  
Performance Models ◽  
Response Models ◽  
Wide Range ◽  
Pavement Response ◽  
Falling Weight

The development of prediction methods for the remaining life of flexible pavements using falling-weight deflectometer (FWD) multiload-level deflections is presented. Pavement response models and pavement performance models were used in developing this procedure. The pavement response models were designed to predict critical pavement responses from surface deflections and deflection basin parameters. The pavement performance models were used to develop the relationships between critical pavement responses obtained from pavement response models and actual pavement performance. Pavement distress data and FWD multiload-level deflection data obtained from the Long-Term Pavement Performance database were used to verify the performance prediction procedure. It was found that the performance of fatigue cracking can be predicted using the proposed procedure except for pavements with high and rapidly increasing cracking in wet-freeze regions. Such trends may be due to environment-induced distresses such as low-temperature cracking, permanent deformation of unbound layers, or both, during the spring thaw period. Predicted rut depths using both single-load and multiload-level deflections show good agreement with measured rut depths over a wide range of rutting potentials. However, the procedure using single–load-level deflections consistently underpredicts the rut depths. This observation demonstrates that the rutting prediction procedure using multiload-level deflections can estimate an excessive level of rutting quite well and thus improve the prediction quality of rutting potential in flexible pavements.

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