Validation of NCAT Structural Test Track Experiment Using INDOT APT Facility

Levenberg, Eyal; McDaniel, Rebecca S; Olek, Jan

doi:10.5703/1288284314311

Cited by 22 publications

(11 citation statements)

References 36 publications

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“…Specimens in these tests were fabricated to different initial compaction levels and moisture contents. Considering all test combinations [28,29], the resilient modulus of the subgrade soil ranged between 31 MPa (4500 psi) and 103 MPa (15,000 psi) and the resilient modulus of the aggregate base ranged between 90 MPa (13,000 psi) and 145 MPa (21,000 psi). These results indicate that within the pavement system, the base layer should exhibit higher stiffness than the subgrade.…”

Section: Resultsmentioning

confidence: 99%

“…These results indicate that within the pavement system, the base layer should exhibit higher stiffness than the subgrade. A complex modulus protocol [30], employing a combination of five different temperature levels and test frequencies, was used for studying the two mix types comprising the HMA layer [28,29,31]. Specimens for these tests were prepared using Gyratory equipment.…”

Section: Resultsmentioning

confidence: 99%

“…Specimens for these tests were prepared using Gyratory equipment. The individual dynamic modulus and phase angle results were later combined into master curves representing the entire HMA layer, taking into account not only the material properties but also the thickness of each lift [19]. After converting the results from the frequency domain to the time domain, a single relaxation modulus master curve was generated.…”

Section: Resultsmentioning

confidence: 99%

“…Solutions to the associated elastic problem were generated using the isotropic layered elastic program ELLEA1 [28,65]. This program accepts as input the thickness and elastic constants of each layer, the loading properties (relative positioning of the tire imprints, radius of each, and stress intensity), and coordinates for which response calculations are desired.…”

Section: Computational Implementationmentioning

confidence: 99%

“…This very idea was pursued previously by the author while interrogating an asphalt pavement system. However, the earlier work was done using oversimplified models in which pavement layers were assumed to be time-independent linear elastic, either isotropic [18,19] or anisotropic [20]. In these studies the overall approach seemed promising, as it was found that systematic errors were minimized when the calibrated model was put in forward calculation mode.…”

Section: Introductionmentioning

confidence: 99%

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Inverse analysis of viscoelastic pavement properties using data from embedded instrumentation

Levenberg

2012

Num Anal Meth Geomechanics

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SUMMARYThe paper deals with inferring the mechanical layer properties of pavement systems by way of inverse analysis. The proposed scheme is based on the availability of sensory gear embedded within the systemcollecting different types of response traces resulting from moving wheel loads. The inverse analysis task simultaneously exploits the readings from all available sensors and therefore formulated as a multicriterion optimization problem. Data from an experimental asphalt pavement are employed to demonstrate the scheme. A versatile and computationally efficient layered viscoelastic pavement model is offered and implemented as a forward solver. Recoverable layer properties, elastic and viscoelastic, are derived and also compared with laboratory values. Results and possible future uses of the work are discussed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Computational Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inverse analysis of viscoelastic pavement properties using data from embedded instrumentation

Levenberg

2012

Num Anal Meth Geomechanics

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Inverse Analysis of Pavement Layer Moduli Based on Data Collected by Buried Accelerometers and Geophones

Bahrani

Levenberg

Blanc

et al. 2020

Lecture Notes in Civil Engineering

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The aim of this study is to develop a method for identifying asphalt pavement layer properties based on readings from accelerometers and geophones that are embedded near the ride surface. These sensors are relatively small in size and easily embeddable, making them an ideal choice for wide-area applications in the live transportation network. As a first step, a section within the IFSTTAR accelerated pavement testing (APT) facility was instrumented with accelerometers and geophones; also installed was an anchored displacement sensor to serve as a reference/validation device. The APT facility offers the ability to control the loading configuration and intensity, travel speed, and wander (i.e., lateral offset) position relative to the sensor locations. Thus, it becomes possible to isolate the task of property identification through inverse analysis from other real-world complications. The paper commences by describing the experimental setup, and presenting some raw sensor measurements during a single pass of the APT's wheel carriage. Then, assuming a layered-elastic model, a method is proposed and demonstrated for estimating the pavement moduli. The method is based on best-matching measured velocities and accelerations for the geophones and accelerometers (respectively), with the model predictions-without integrating the signals to convert them into deflections. Very good match is obtained for the sensor readings, and the inferred moduli closely agree with reference values. This outcome means that there is great potential in building a pavement condition monitoring system with nearsurface accelerometers and geophones.

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Characterization of asphalt concrete linear viscoelastic behavior utilizing Havriliak–Negami complex modulus model

Sun

Chen

Huang

2015

Construction and Building Materials

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Validation of NCAT Structural Test Track Experiment Using INDOT APT Facility

Cited by 22 publications

References 36 publications

Inverse analysis of viscoelastic pavement properties using data from embedded instrumentation

Inverse analysis of viscoelastic pavement properties using data from embedded instrumentation

Inverse Analysis of Pavement Layer Moduli Based on Data Collected by Buried Accelerometers and Geophones

Characterization of asphalt concrete linear viscoelastic behavior utilizing Havriliak–Negami complex modulus model

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