Preliminary Local Calibration of Performance Prediction Models in AASHTOWare Pavement ME Design for Flexible Pavement Rehabilitation in Oregon

Rahman, Shaidur; Podolsky, Joseph H.; Scholz, T

doi:10.1061/jpeodx.0000114

Cited by 8 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dynamic modulus is essentially the normal value of complex modulus. e value is obtained by dividing the maximum (peak-to-peak) stress by the recoverable (peak-to-peak) axial strain for the HMA material subject to a sinusoidal loading, as evident in equation (3). e average diameter of the test specimen shall be between 100 and 104 mm with a standard deviation of 1.0 mm.…”

Section: E Dynamic Modulusmentioning

confidence: 99%

“…Rutting relationships with other distress types of the asphalt pavement are analyzed by the association rule mining method [2]. Rahman et al calibrated rutting, alligator cracking, longitudinal cracking, and thermal cracking prediction models of the hot-mix asphalt (HMA) overlays for Oregon conditions [3]. e Newton-Raphson iteration procedure is employed to calculate the permanent deformation [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of the Rutting Performance of the Field Specimen Using the Hamburg Wheel‐Tracking Test and Dynamic Modulus Test

Dai

Jia

Wang

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

Rutting is a major distress occurring in the service life of the asphalt pavement, especially in hot weather areas. A laboratory-produced specimen is widely used for rutting performance evaluation which may not be completely represented by the field situation. The objective of this study is to evaluate the rutting performance of field specimens from the Chongqing highway by utilizing the Hamburg wheel-tracking test (HWTT) and dynamic modulus test. Different test conditions were conducted on the HWTT by investigation of the actual local weather condition. The results showed that rutting depth was different under different test conditions, and 10000 loading cycles were recommended as the maximum loading cycles. Particularly, several factors that influence the rutting depth were investigated, and the specimen height of 6 cm is more appropriate for the HWTT. Additionally, different test conditions were proposed as the HWTT test condition for different asphalt concrete (AC) layers in the Chongqing area. Rutting contribution of each AC layer to the pavement structure was analyzed. Moreover, the dynamic modulus at 54.4°C, 5 Hz and 54.4°C, 1 Hz could effectively represent the rutting performance of the asphalt mixture, and the dynamic modulus test is recommended for the rutting performance evaluation of the full-thickness AC layer.

show abstract

Section: E Dynamic Modulusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the Rutting Performance of the Field Specimen Using the Hamburg Wheel‐Tracking Test and Dynamic Modulus Test

Dai

Jia

Wang

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…Research results can be found in the long-term performance evaluations [ 23 , 24 , 25 , 26 ] and distress mechanism investigations [ 27 , 28 ] of pavement materials and structures, based on the APT technique, whereas there are lacking studies on the calibration methods of rut prediction models under complex test conditions, based on APT. Hence, calibrated M-E models, recommended in the design guide or provided by similar projects, are commonly used for IAPs in current research and engineering practice [ 29 , 30 ], which may not be applicable to the variety of pavement material types, structural combination, environment, and traffic characteristics of the local projects. Moreover, an accumulative RD analysis and prediction model construction generally utilizes Miner’s law [ 31 ], in which the linear superposition principle of this theory fails to include the impacts of the pavement material’s nonlinearity and loading history on the rut progression.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Inverted Asphalt Pavement Rut Prediction Model, Based on Full-Scale Accelerated Pavement Testing

Han

Sha

et al. 2023

Materials

View full text Add to dashboard Cite

This study investigates the establishment and calibration method of the rut depth (RD) prediction model of inverted asphalt pavements (IAPs), based on full-scale accelerated pavement testing (APT), which facilitates the accurate and reliable design or maintenance of IAPs. A power function is adopted for the prediction model construction of the rut progression before the failure stage, based on the typical permanent deformation progression curve of flexible pavements. The APT loading history is divided into units, according to the difference in physical conditions, providing the basis for a cumulative RD analysis and model calibration. The nonlinear incremental recursive (IR) principle is applied in the RD analysis to consider the influence of the nonlinear material property, performance deterioration, and loading history on the RD development. Further, the rut shift function relating prediction models obtained from laboratory tests and full-scale APT is established to introduce the APT data in the calibration process. Accordingly, the mechanistic-empirical RD prediction model calibration method, based on APT and the IR principle, is proposed and applied in a case study of a IAP RD prediction model calibration. Four 3.5 m × 4 m IAP test sections S1–S4 are constructed and instrumented and 700,000- and 900,000-wheel loads are applied on test sections S1–S2 and S3–S4, respectively, using the heavy vehicle simulator. The test data from the different APT load units are utilized for the model calibration, and the resultant prediction errors range from −2.16 mm to 1.18 mm. The calibrated model can also be used for the RD prediction of IAPs with other design schemes, by updating the corresponding material-related coefficients and the finite element model, which is essential for the design and maintenance of IAPs. The proposed calibration method could be a useful reference for the establishment of flexible pavement performance prediction models.

show abstract

“…Research in flexible pavements, which are the most commonly used asphalt structures for low to medium volume traffic, has shown that they are sensitive to rutting damage and thermal cracking under heavy loads [10,29]. This type of pavement transmits surface loading stresses to the subbase layer by grain-to-grain transfer through the points of contact.…”

mentioning

confidence: 99%

A data-driven approach to the evaluation of asphalt pavement structures using falling weight deflectometer

Liu

Cao

Huang

et al. 2022

DCDS-S

View full text Add to dashboard Cite

<p style='text-indent:20px;'>The evaluation of asphalt pavement structures has been a critical challenge in the field due to the practical limitations in methodology. In this paper, we propose a data-driven framework to evaluate structural performance of nineteen widely used asphalt structures in the Research Institute of Highway Ministry of Transport track (RIOHTrack). Specifically, we utilize the unsupervised machine learning method to delineate the similar and disparate performance among tested structures based on four years of falling weight deflectometer (FWD) experiments. Next, the structural performance is investigated on the temporal scale and the dynamic performance variations are captured over the course of the testing. Finally, experimental results are discussed and we provide essential evidence to aid future asphalt pavement design and construction.</p>

show abstract

Preliminary Local Calibration of Performance Prediction Models in AASHTOWare Pavement ME Design for Flexible Pavement Rehabilitation in Oregon

Cited by 8 publications

References 11 publications

Evaluation of the Rutting Performance of the Field Specimen Using the Hamburg Wheel‐Tracking Test and Dynamic Modulus Test

Evaluation of the Rutting Performance of the Field Specimen Using the Hamburg Wheel‐Tracking Test and Dynamic Modulus Test

Calibration of Inverted Asphalt Pavement Rut Prediction Model, Based on Full-Scale Accelerated Pavement Testing

A data-driven approach to the evaluation of asphalt pavement structures using falling weight deflectometer

Contact Info

Product

Resources

About