Structural Evaluation of Subbase Layers at Different Test Sites using a Falling Weight Deflectometer

2020 ◽  
Vol 22 (2) ◽  
pp. 1-5
Author(s):  
Tae-Soon Park ◽  
Il-Hwan Kang
Author(s):  
Mario S. Hoffman

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.


Author(s):  
Christoffer P. Nielsen

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.


2017 ◽  
Vol 23 (3) ◽  
pp. 338-346 ◽  
Author(s):  
Amir KAVUSSI ◽  
Mojtaba ABBASGHORBANI ◽  
Fereidoon MOGHADAS NEJAD ◽  
Armin BAMDAD ZIKSARI

Pavement condition assessment at network level requires structural evaluation that can be achieved using Falling Weight Deflectometer (FWD). Upon analysing FWD data, appropriate maintenance and repair methods (preser­vation, rehabilitation or reconstruction) could be assigned to various pavement sections. In this study, Structural Condi­tion Index (SCI), defined as the ratio of Effective Structural Number (SNeff) to Required Structural Number (SNreq), was used to determine if a pavement requires preservation or rehabilitation works (i.e. preservation SCI > 1, rehabilitation SCI < 1). In addition to FWD deflection data, SCI calculation requires pavement layer thicknesses that is obtained using GPR with elaborated and time consuming works. In order to reduce field data collection and analysis time at network-level pavement management, SCI values were calculated without having knowledge of pavement layer thicknesses. Two regression models were developed based on several thousand FWD deflection data to calculate SNeff of pavements and resilient modulus (MR) of their subgrades. Subgrades MR values together with traffic data were then used to calculate SNreq. Statistical analysis of deflection data indicated that Area under Pavement Profile (AUPP) and the deflection at distance of 60 cm from load center (D60) parameters showed to have strong correlation with SNeff and MR respectively. The determination coefficients of the two developed models were greater than those of previous models reported in the literature. The significant result of this study was to calculate SNeff and MR using the same deflection data. Finally, imple­mentation of the developed method was described in determining appropriate Maintenance and Repair (M&R) method at network level pavement management system.


Author(s):  
Douglas Steele ◽  
Hyung Suk Lee ◽  
Curt Beckemeyer ◽  
Thomas Van

Traffic speed deflection devices (TSDDs) have been developed since around 2000 to allow for safe and efficient structural evaluation of highway networks. One barrier to TSDD implementation is the inherent differences in deflections produced by moving truck loads and by falling weight deflectometer (FWD), the current deflection testing standard. To better understand the differences in data produced by the two devices, FHWA sponsored research into one particular TSDD, the rolling wheel deflectometer (RWD). The study utilized the finite layer program ViscoWave to model both FWD and RWD loads to demonstrate the effect of their inherent differences on pavement deflections and other simulated parameters. In addition, ViscoWave was used to generate theoretical FWD and RWD deflections for a diverse set of pavement structures and subgrade conditions. The resulting deflections were used to develop correlations between the two devices, which were validated with side-by-side FWD and RWD field tests performed on 23 sites. The research determined that the differences between FWD and RWD deflections vary depending on pavement factors and loading characteristics. The two devices produced similar deflections on thicker, stiffer, lower-deflection pavements, while the FWD produced relatively higher deflections on thinner, weaker, higher-deflection pavements. Therefore, use of common FWD data analysis programs will produce different results, such as layer moduli, for TSDD devices. Advanced analysis routines capable of modeling the TSDD’s moving load and loading configurations are needed.


Author(s):  
Shivesh Shrestha ◽  
Samer W. Katicha ◽  
Gerardo W. Flintsch ◽  
Senthilmurugan Thyagarajan

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.


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