Robust Deflection Indices from Traffic-Speed Deflectometer Measurements to Predict Critical Pavement Responses for Network-Level Pavement Management System Application

Nasimifar, Mahdi; Thyagarajan, Senthilmurugan; Siddharthan, Raj; Sivaneswaran, Nadarajah

doi:10.1061/(asce)te.1943-5436.0000832

Cited by 38 publications

(13 citation statements)

References 11 publications

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“…The 3D-Move program uses a finite layer approach and Fourier transform technique for estimating pavement responses. The outputs from 3D-Move have been validated using field-measured responses in previous studies ( 15 – 17 ). In addition, 3D-Move is capable of incorporating the viscoelastic properties of AC layers, non-uniform contact pressure, and vehicle speed, while conventional backcalculation programs consider only stationary FWD loading configurations.…”

Section: Introductionmentioning

confidence: 99%

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

Zihan

Elseifi

Icenogle

et al. 2020

Transportation Research Record

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zihan

Elseifi

Icenogle

et al. 2020

Transportation Research Record

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“…As a direct indicator of the material strength, modulus occurs in many ME models to predict the total number of load repetitions a pavement can bear 3,12 . Deflection, directly controlled by the modulus, is used to calculate indices determining the pavement condition such as the structural number (SN), 13,14 the structural condition index (SCI), 10 the structural index of a pavement (SIP), 15 and the deflection slope index (DSI) 16 . The development of nondestructive testing (NDT) devices such as the falling weight deflectometer (FWD), the rolling wheel deflectometer (RWD), and the traffic speed deflectometer (TSD) contributes to the obtain and applications of these indices.…”

Section: Introductionmentioning

confidence: 99%

Determination of flexible pavement deterioration conditions using Long‐Term Pavement Performance database and artificial intelligence‐based finite element model updating

Deng

Luo

Zhang

et al. 2020

Struct Control Health Monit

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“…TSD‐measured deflections can be used for monitoring pavement structure condition in PMS. The deflection‐based index can be derived to detect the weak pavement section with smaller stiffness due to internal damage, such as structure number (SN) (Flintsch et al., 2013) and surface curvature index (SCI300) (Nasimifar, Thyagarajan, Siddharthan, & Sivaneswaran, 2016). This provides the advantage of using TSD at the network level.…”

Section: Introductionmentioning

confidence: 99%

Asphalt pavement modulus backcalculation using surface deflections under moving loads

Wang

Zhao

et al. 2020

Computer aided Civil Eng

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The measurement of pavement surface deflections under moving loads can be used for evaluation of pavement structural condition. The objective of this paper is to develop an innovative backcalculation method to calculate pavement layer moduli based on traffic speed deflectometer (TSD) measurements and analyze factors influencing backcalculation results. The backcalculation method is based on 2.5D finite element method (FEM) and constrained extended Kalman filter (CEKF). Field measurements at different pavement sections were used to verify the accuracy of backcalculation results. Parametric analysis was conducted to evaluate the influences of traffic loading and pavement structure variations in the field. It was found that the backcalculation results were considerably affected by the variation of asphalt layer thickness and dynamic loading due to pavement surface roughness. A heavy truck passing the adjacent lane during TSD testing could also cause variation of backcalculation results.

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Robust Deflection Indices from Traffic-Speed Deflectometer Measurements to Predict Critical Pavement Responses for Network-Level Pavement Management System Application

Cited by 38 publications

References 11 publications

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

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

Determination of flexible pavement deterioration conditions using Long‐Term Pavement Performance database and artificial intelligence‐based finite element model updating

Asphalt pavement modulus backcalculation using surface deflections under moving loads

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