Pavement Rehabilitation Selection Based on Mechanistic Analysis and Field Diagnosis of Falling Weight Deflectometer Data: Virginia Experience

Zaghloul, Sameh; Elfino, Mohamed

doi:10.3141/1730-21

Cited by 11 publications

(6 citation statements)

References 1 publication

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“…percentage of cracking or depth of rut) with simple remaining life algorithms to estimate the RSL (Ferregut, Abdallah, Melchor, & Nazarian, 1999). Zaghloul and Elfino utilized expected traffic and back-calculated layer moduli to predict the RSL (Zaghloul & Elfino, 2000). Gedafa suggested sigmoidal models for estimating RSL based on the central deflection from a rolling wheel reflectometer (RWD) or FWD (Gedafa, 2008).…”

Section: An Overview Of the Rsl Models Of Pavementmentioning

confidence: 99%

Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)

Karballaeezadeh

Shamshirband

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Accurate prediction of the remaining service life (RSL) of pavement is essential for the design and construction of roads, mobility planning, transportation modeling as well as road management systems. However, the expensive measurement equipment and interference with the traffic flow during the tests are reported as the challenges of the assessment of RSL of pavement. This paper presents a novel prediction model for RSL of road pavement using support vector regression (SVR) optimized by particle filter to overcome the challenges. In the proposed model, temperature of the asphalt surface and the pavement thickness (including asphalt, base and sub-base layers) are considered as inputs. For validation of the model, results of heavy falling weight deflectometer (HWD) and groundpenetrating radar (GPR) tests in a 42-km section of the Semnan-Firuzkuh road including 147 data points were used. The results are compared with support vector machine (SVM), artificial neural network (ANN) and multi-layered perceptron (MLP) models. The results show the superiority of the proposed model with a correlation coefficient index equal to 95%.

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Section: An Overview Of the Rsl Models Of Pavementmentioning

confidence: 99%

Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)

Karballaeezadeh

Shamshirband

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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show abstract

“…Several factors should be considered in pavement sectioning, such as pavement type, construction history, and future traffic. Information on how pavement sectioning can be done at the network and project levels has been presented previously (1,5). Once the limits of structurally homogeneous segments are identified, a representative layer thickness is assigned to each homogeneous section.…”

Section: Reliability-based Backcalculation Analysismentioning

confidence: 99%

Implementation of Reliability-Based Backcalculation Analysis

Zaghloul

Helali

Ahmed

et al. 2005

Transportation Research Record: Journal of the Transportation R

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The reliability concept provides a means of incorporating some degree of certainty into the pavement design process to ensure that the outcomes of the process will provide acceptable levels of service until the end of the intended design life. Pavement structural performance and rehabilitation design are highly dependent on the in situ layer properties. Pavement layer thickness is an essential input in backcalculation analysis performed with measured surface deflections to evaluate the in situ structural capacity of a pavement. Inaccurate thickness information may lead to significant errors in the backcalculated layer moduli and, hence, in the rehabilitation design. Because pavement layer thickness has some degree of variability (normal variability), it is important to consider this variability in the backcalculation analysis and rehabilitation design. A procedure was developed to implement the reliability concept in backcalculation analysis to account for the normal variability in layer thickness within structurally homogeneous sections. This procedure was developed on the basis of in situ layer information obtained from a ground-penetrating radar study performed for the New Jersey Department of Transportation. This paper provides an overview of the procedure, along with the results of the pilot implementation of the procedure. This reliability procedure complements the reliability factor of the 1993 AASHTO pavement design guide, as the latter reliability factor does not account for the in situ layer thickness.

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“…Sameh Zaghloul, Tom Hoover, D. J. Swan, Nick Vitillo, Robert Sauber, and Andris A. Jumikis applicable at both the network level and the project level. More information about the implementation of this technique at these levels can be found in Zaghloul et al and Zaghloul and Elfino (2,3).…”

Section: Enhancing Backcalculation Procedures Through Consideration Of Thickness Variabilitymentioning

confidence: 99%

Enhancing Backcalculation Procedures Through Consideration of Thickness Variability

Zaghloul

Hoover

Swan

et al. 2004

Transportation Research Record: Journal of the Transportation R

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Pavement structural performance and in situ structural capacity are evaluated by performing deflection testing and backcalculation analysis. Layer thickness information obtained from as-built data and core-bore logs is an essential input for this analysis and has significant impact on the backcalculated layer moduli. However, as-built records are not always available, and they provide general layer thickness information. Also, the number of cores and bores is limited because of their destructive nature. The current practice of backcalculation uses the common assumption that a pavement section has constant layer thickness, with allowable tolerance ignored, and that all variability in the measured deflection is related to variability in material stiffness. A pavement section is not expected to have identical thickness along its length. Also, pavement materials are undeniably nonhomogeneous and have variable stiffness along section length. However, this variability is expected to be within reasonable limits, especially for relatively new pavements constructed in accordance with stringent construction specifications and material properties and which exhibit similar distresses and deflections. A procedure was developed to account for the normal variability in layer thickness (allowable tolerance). This procedure is outlined and an implementation example is provided. The impact of ignoring the allowed variability in layer thickness on the backcalculation results, as well as on the design pavement moduli that are used in the rehabilitation analysis and design, is discussed.

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Pavement Rehabilitation Selection Based on Mechanistic Analysis and Field Diagnosis of Falling Weight Deflectometer Data: Virginia Experience

Cited by 11 publications

References 1 publication

Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)

Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)

Implementation of Reliability-Based Backcalculation Analysis

Enhancing Backcalculation Procedures Through Consideration of Thickness Variability

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