Comparing Traffic Speed Deflectometer and Falling Weight Deflectometer Data

Levenberg, Eyal; Pettinari, Matteo; Baltzer, S; Christensen, Britt Marie Lekven

doi:10.1177/0361198118768524

Cited by 20 publications

(18 citation statements)

References 15 publications

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“…Structural evaluation of the pavement network is one of the requirements of pavement management systems and FWD is the most common test for structural evaluation of pavement (Levenberg et al 2018). This test is widely used by pavement engineers due to the desirable simulation of traffic load.…”

Section: Falling Weight Deflectometer (Fwd)mentioning

confidence: 99%

Usage Surface Deflection Data for Performance Prediction in Flexible Pavement

Karballaeezadeh¹,

Zaremotekhases²,

Nabipour³

et al. 2019

Preprint

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The conventional method used for calculating pavement condition index (PCI) has two major drawbacks: safety problems during pavement inspection, and human error. This paper proposes a method for removing these problems. The proposed method uses surface deflection data in falling weight Deflectometer test to estimate PCI. The data used in this study were derived from 236 pavement segments taken from Tehran-Qom freeway in Iran. The data set was analyzed using multi layers perceptron (MLP) and radial basis function (RBF) neural networks. These neural networks were optimized by levenberg-marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic (RBF-GA) algorithms. After initial modeling with four neural networks mentioned, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of analysis have been verified by the four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SD). The best reported results belonged to CMIS, including APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.

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Section: Falling Weight Deflectometer (Fwd)mentioning

confidence: 99%

Usage Surface Deflection Data for Performance Prediction in Flexible Pavement

Karballaeezadeh¹,

Zaremotekhases²,

Nabipour³

et al. 2019

Preprint

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“…Elseifi, Zihan, & Icenogle (2019) proposed the approach to utilize TSD test measurements for back-calculation analysis. Levenberg, Pettinari, Baltzer, & Christensen (2018) proposed the deflection index for assessing the TSD data. As loading types for FWD and TSD tests differ, a certain correlation method for resistance parameters should be developed.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of the Modulus of Deformation Determined by Static and Dynamic Plate Load Tests

Lehmann

Leppla²,

Norkus

2020

BJRBE

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Soil, or soil structure modulus of deformation, is one of the main design parameters for road engineering and traffic infrastructure design of, for example, highways, railways, runways and embankments. It is also the main soil improvement criterion. When creating any road structure with codified design resistance, one employs structural layers of certain thicknesses and modulus of deformation. Both values need to satisfy the minimum values in accordance with codified requirements. This paper analyzes correlations for the widely applied in engineering practice methods to determine the soil stiffness. The static test methods acknowledged to be exact enough for determining the modulus of deformation for the primary and secondary loadings. As dynamic test methods require significantly less time and financial resources, they are widely accepted in engineering practice. The dynamic methods determine only the dynamic modulus of deformation. Design practice aims to relate it with the static modulus of deformation of the secondary loading. Many countries propose codified correlations, with differing levels of conservatism, to convert the dynamic modulus of deformation into the static one. Developed correlations between the results of the static plate load test and the dynamic plate load tests processed from own test results of different soils are presented and a comparative analysis with other proposed correlations is given.

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“…It appears that present-day computing powers and sensing technologies are advanced to the point that allow building such platforms, first and foremost for pavement management applications (Elseifi, Gaspard, Wilke, Zhang, & Hegab, 2015;Levenberg, Pettinari, Baltzer, & Christensen, 2018;Muller & Roberts, 2013;Nasimifar, Siddharthan, Hajj, & Motamed, 2016;Zofka, Sudyka, Maliszewski, Harasim, & Sybilski, 2014). At the same time the technology is rapidly approaching project-level capabilities of inferring individual elastic layer properties (Andersen, Levenberg, & Andersen, 2017;Nasimifar, Thyagarajan, & Sivaneswaran, 2017).…”

Section: Introductionmentioning

confidence: 99%

Analysis of a moving measurement platform based on line profile sensors for project-level pavement evaluation

Skar

Levenberg

Andersen

et al. 2020

Road Materials and Pavement Design

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In recent years there has been renewed interest in developing Moving Measurement Platforms (MMPs) to replace the Falling Weight Deflectometer (FWD) which currently serves as the industry standard for project-level pavement evaluation. The purpose of this study was to contribute to the transition efforts from FWD to MMPs. While focusing on a new MMP device (called Raptor) based on line profile sensing technology, the work addressed three fundamental aspects needed for eventual field data assessment and industry acceptance: (i) loading and measurement setup -presenting an innovative device consisting of a beam equipped with several line profilers and also motion tracking sensors. By means of image analysis this configuration allows for tracking a point on the pavement surface and measuring displacement information while moving. (ii) interpretation method -capable of accepting devicemeasured data as input, and providing elastic properties for a layered pavement model. (iii) sensitivity to measurement errors -where the interpretation method is first validated and then interrogated for error effects on the inferred moduli. Overall, the obtained results provide basic confidence in the device and the interpretation method, and demonstrate that the technology has the potential to meet project-level pavement evaluation needs.

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Comparing Traffic Speed Deflectometer and Falling Weight Deflectometer Data

Cited by 20 publications

References 15 publications

Usage Surface Deflection Data for Performance Prediction in Flexible Pavement

Usage Surface Deflection Data for Performance Prediction in Flexible Pavement

Experimental Study of the Modulus of Deformation Determined by Static and Dynamic Plate Load Tests

Analysis of a moving measurement platform based on line profile sensors for project-level pavement evaluation

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