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

Zihan, Zia U. A.; Elseifi, Mostafa A.; Icenogle, Patrick; Gaspard, Kevin; Zhang, Zhongjie

doi:10.1177/0361198120914296

Cited by 17 publications

(9 citation statements)

References 14 publications

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“…Even if statistical measures show that TSD and FWD produce statistically different measurements, most past studies have shown that, from an engineering and practical perspective, the two devices show similar trends in the structural variation along a tested road even when the numerical values of the measured deflection are not the same (e.g., Figure 9 and Figure 10) ( 5 , 35 ). These results suggest that, for network-level applications, TSD measurements can replace FWD measurements, and that good empirical models can be developed between TSDD measurements and FWD measurements for network-level applications ( 2 , 6 , 39 – 43 ). The drawback of empirical models is that they cannot be generalized to conditions other than those with which the models were developed.…”

Section: Evaluation Of Tsdmentioning

confidence: 90%

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

Katicha

Flintsch

Diefenderfer

2022

Transportation Research Record: Journal of the Transportation R

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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.

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Section: Evaluation Of Tsdmentioning

confidence: 90%

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

Katicha

Flintsch

Diefenderfer

2022

Transportation Research Record: Journal of the Transportation R

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“…11 Since TSD is a technology developed after FWD, most of the stiffness back-calculation methods based on TSD deflection data are to first convert TSD deflection data into FWD deflection data through correlation analysis, and then utilize the aforementioned FWD-based stiffness back-calculation method to identify the stiffness of pavement layers. 12–14…”

Section: Introductionmentioning

confidence: 99%

Stiffness identification method for asphalt pavement layers and interfaces using monitoring data from built-in sensors

Zhang

Dong

2022

Structural Health Monitoring

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The bearing capacity of asphalt pavement gradually deteriorates due to repeated traffic loads. As the crucial mechanical index to evaluate the bearing capacity, the evolution of the stiffness of pavement layers and interfaces is necessary to be mastered. This paper proposes a stiffness identification method for the asphalt pavement layers and interfaces by using monitoring data from built-in sensors. First, the analytical solution of multi-layered elastic/viscoelastic medium with imperfect interlayer subjected to moving load is derived, and the theoretical relationship between stiffness and mechanical response can be obtained. The sensor layout is optimized on the basis of the theoretical relationship. Then, the stiffness identification method is developed, and its feasibility is theoretically explored through an example of three-layered elastic/viscoelastic medium. Finally, the proposed stiffness identification method is applied to a realistic asphalt pavement to validate its reliability. Results show that the proposed method can evaluate the stiffness of pavement layers (including elastic and viscoelastic properties) and interfaces. It is noteworthy that the stiffness identification method by using monitoring data from built-in sensors can be performed in real-time under each passing vehicle load, and is helpful to understand the damage behavior of pavement and guide maintenance decision-making.

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“…Although FWD and TSD deflections are not equal in magnitude, research studies have been conducted to define their statistical and numerical correlations ( 6 , 7 ). The main objective of these studies was to incorporate TSD measurements, after converting them into the corresponding FWD deflections, in a backcalculation analysis because backcalculation is typically conducted based on FWD deflections.…”

Section: Introductionmentioning

confidence: 99%

“…The model showed acceptable accuracy with a coefficient of determination (R 2 ) of 0.81 and 0.90 in the training and validation phase, respectively ( 6 ). Similarly, Zihan et al ( 7 ) used TSD measurements, thickness data, and pavement layers moduli to predict the corresponding FWD deflection basins using ANNs. For this study, a validated 3D-Move model was used to calculate theoretical FWD and TSD deflection basins for 162 pavement sections, which represented typical pavement designs in Louisiana.…”

Section: Introductionmentioning

confidence: 99%

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

Abohamer

Elseifi

Zihan

et al. 2021

Transportation Research Record: Journal of the Transportation R

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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.

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

Cited by 17 publications

References 14 publications

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

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

Stiffness identification method for asphalt pavement layers and interfaces using monitoring data from built-in sensors

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

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