Effects of Pavement-Falling Weight Deflectometer Interaction on Measured Pavement Response

2009 ◽  
pp. 326-326-15 ◽  
Author(s):  
KM Boddapati ◽  
S Nazarian
Keyword(s):  
Falling Weight Deflectometer ◽  
Pavement Response ◽  
Falling Weight

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.

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.

scholarly journals Flexible pavement damage during spring thaw: A field study using the falling weight deflectometer

2018 ◽  
Vol 45 (3) ◽  
pp. 227-234 ◽  
Author(s):  
Jean-Pascal Bilodeau ◽  
Guy Doré
Keyword(s):  
Fatigue Damage ◽  
Load Reduction ◽  
Falling Weight Deflectometer ◽  
Material Condition ◽  
Structural Capacity ◽  
Vehicle Loading ◽  
Pavement Response ◽  
Pavement Damage ◽  
Falling Weight ◽  
Performance Conditions

Spring thaw creates critical performance conditions for pavement networks. The increase of water content in the pavement environment is significant during spring thaw. Combined with poor drainage conditions, material condition variations are triggering factors that accentuate the effect of heavy vehicle loading on pavement response and damage. Two experimental pavement sections were monitored in 2014 and 2015 for temperature and deflections. The section with the lowest structural capacity was found to be more sensitive to thaw weakening. Fatigue damage calculated for this section was found to be 31% higher than the section with the highest structural capacity. Moreover, it was shown that a load reduction in the range of 20% can decrease the total yearly damage by about 7 to 10% for the considered test sections. In general, fatigue damage was found to increase from spring onset to the warmest conditions of the yearly cycle, in July.

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.

DIPLOBACK: Neural-Network-Based Backcalculation Program for Composite Pavements

1997 ◽  
Vol 1570 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Lev Khazanovich ◽  
Jeffery Roesler
Keyword(s):  
Neural Network ◽  
Winkler Foundation ◽  
Multilayer Composite ◽  
Falling Weight Deflectometer ◽  
Subgrade Reaction ◽  
Elastic Parameters ◽  
The Neural Networks ◽  
Elastic Layers ◽  
Falling Weight ◽  
Moduli Of Elasticity

A neural-network-based backcalculation procedure is developed for multilayer composite pavement systems. The constructed layers are modeled as compressible elastic layers, whereas the subgrade is modeled as a Winkler foundation. The neural networks are trained to find moduli of elasticity of the constructed layers and a coefficient of subgrade reaction to accurately match a measured deflection profile. The method was verified by theoretically generated deflection profiles and falling weight deflectometer data measurements conducted at Edmonton Municipal Airport, Canada. For the theoretical deflection basins, the results of backcalculation were compared with actual elastic parameters, and excellent agreement was observed. The results of backcalculation using field test data were compared with the results obtained using WESDEF. Similar trends were observed for elastic parameters of all the pavement layers. The backcalculation procedure is implemented in a computer program called DIPLOBACK.

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