Fatigue Performance Prediction of Asphalt Pavements with FlexPAVETM, the S-VECD Model, and DR Failure Criterion

2018 ◽  
Vol 2672 (40) ◽  
pp. 217-227 ◽  
Author(s):  
Yizhuang David Wang ◽  
Behrooz Keshavarzi ◽  
Y. Richard Kim
Keyword(s):  
Performance Prediction ◽  
Failure Criterion ◽  
Asphalt Pavement ◽  
Moving Load ◽  
Field Measurements ◽  
The United States ◽  
Climatic Conditions ◽  
Fatigue Performance ◽  
Pavement Performance ◽  
Finite Element Program

Reliable predictions of asphalt materials and pavement performance are important elements in mixture design, mechanistic-empirical pavement design, and performance-related specifications. This paper presents FlexPAVE™, a pavement performance prediction program. FlexPAVE™ is a three-dimensional finite element program that is capable of moving load analysis under realistic climatic conditions. It utilizes the simplified viscoelastic continuum damage (S-VECD) model to predict asphalt pavement fatigue life. This S-VECD model currently incorporates the so-called GR failure criterion to define the failure of asphalt mixtures. In this study, a new failure criterion for the S-VECD model, designated as the DR criterion, has been developed to remedy some of the shortcomings of the GR failure criterion. This DR criterion has been implemented successfully in FlexPAVETM. In this paper, FlexPAVETM is used to simulate the fatigue performance of field test sections. These test sections include various pavement structures, such as perpetual pavements and accelerated load facility test pavements in the United States, South Korea, and China, as well as various materials, such as warm-mix asphalt, reclaimed asphalt pavement, and mixtures with modified binders. The DR-based FlexPAVETM predictions have yielded good agreement with the field measurements and show more reasonable trends compared to predictions obtained using the GR failure criterion.

scholarly journals A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuancang Wang ◽  
Jing Zhao ◽  
Qiqi Li ◽  
Naren Fang ◽  
Peicheng Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Performance Prediction ◽  
Asphalt Pavement ◽  
Pavement Performance ◽  
Support Vector ◽  
Grey Relation Analysis ◽  
Relation Analysis ◽  
Pavement Performance Prediction ◽  
Grey Relation

Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction.

Suitability of Asphalt Pavement Analyzer for Predicting Pavement Rutting

2000 ◽  
Vol 1723 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Bouzid Choubane ◽  
Gale C. Page ◽  
James A. Musselman
Keyword(s):  
Performance Prediction ◽  
Field Data ◽  
Asphalt Pavement ◽  
Evaluation Process ◽  
Field Measurements ◽  
Asphalt Mixtures ◽  
Test Results ◽  
Asphalt Mixes ◽  
Tracking Device ◽  
Wheel Tracking

Findings are summarized from an investigation performed to evaluate the suitability of a wheel-tracking device known as the asphalt pavement analyzer (APA) for assessing the rutting potential of asphalt mixes. The evaluation process consisted of correlating the APA’s predicted rutting with known field measurements. The correlation between beam and gyratory samples and the testing variability were also investigated. In addition, the APA test results were compared with those obtained using the Georgia loaded-wheel tester. The findings of this investigation indicated that the APA may be an effective tool to rank asphalt mixtures in terms of their respective rut performance. However, for each mixture type, the APA testing variability was significant between tests and between the three testing locations within each test. Differences in rut measurements of up to 4.7 and 6.3 mm were recorded for beam and gyratory samples, respectively. Therefore, using the APA as a clear pass-or-fail criterion for performance prediction purposes of asphalt mixtures may not be appropriate at this time. It should be noted that these findings are based on data collected on three mixes. Therefore, it is suggested that the APA testing variability (testing and testing locations within the device) be further assessed with a wider range of mixtures. The intent of such an assessment should not only be to correlate the APA results with field data but also to develop potential pass-or-fail limits and procedures.

Integrating potential climate change into the mechanistic–empirical based pavement design

2013 ◽  
Vol 40 (12) ◽  
pp. 1173-1183 ◽  
Author(s):  
Qiang Joshua Li ◽  
Leslie Mills ◽  
Sue McNeil ◽  
Nii O. Attoh-Okine
Keyword(s):  
Climate Change ◽  
United States ◽  
The United States ◽  
Adaptation Strategy ◽  
Climatic Conditions ◽  
Pavement Design ◽  
Pavement Performance ◽  
Climate Change Effects ◽  
And Performance ◽  
Potential Climate Change

Given anticipated climate change and its inherent uncertainty, a pavement could be subjected to different climatic conditions over its life and might be inadequate to withstand future environmental stresses beyond those currently considered during pavement design. This paper incorporates climate change effects into the mechanistic–empirical (M-E) based pavement design to explore potential climate change and its uncertainty on pavement design and performance. Three important questions are addressed: (1) How does pavement performance deteriorate differently with climate change and its uncertainty? (2) What is the risk if climate change and its uncertainty are not considered in design? and (3) How do pavement designers respond and incorporate this change into M-E design ? Three test sites in the United States are examined and results demonstrate a robust and effective approach to integrate climate change into pavement design as an adaptation strategy.

Research on Performance Prediction of Highway Asphalt Pavement Based on Grey–Markov Model

2021 ◽  
pp. 036119812110575
Author(s):  
Yunsheng Zhu ◽  
Jinxu Chen ◽  
Kaifeng Wang ◽  
Yong Liu ◽  
Yanting Wang
Keyword(s):  
Markov Model ◽  
Prediction Model ◽  
Performance Prediction ◽  
Asphalt Pavement ◽  
Condition Index ◽  
Economic Benefits ◽  
Pavement Performance ◽  
Prediction Errors ◽  
Grey Prediction ◽  
Relative Errors

Reasonable and accurate forecasts can be used by the highway maintenance management department to determine the best maintenance timing and strategy, which can keep the highway performing well and maximize its social and economic benefits. A Grey–Markov combination model is established in this paper to predict highway pavement performance accurately based on the Grey GM (1, 1) model (a single-variable Grey prediction model with a first-order difference equation) and revised by the Markov model. The advantages of the short-term forecast Grey model and the probabilistic Markov model, which considers the fate of pavement performance prediction, are comprehensively applied to the combined forecasting model. The Grey GM (1, 1), Grey–Markov model and Liu-Yao model are adopted to predict the pavement condition index (PCI) based on the actual PCI values measured in Shanxi, Chongqing, and Shaoguan. The average relative errors of the above three models’ predicted values in Shanxi are 0.73%, 1.18%, and 0.67%, respectively, from 2012 to 2014. Thus, the prediction errors of the three models are relatively close. The average relative errors of the prediction values predicted by the three models are 3.89%, 0.67%, and 0.50%, respectively, from 2015 to 2019. The latter two errors are more minor than the Grey GM (1, 1) model. Two other regions have similar conclusions. The results show that the prediction accuracy of the combination Grey–Markov prediction model established in this paper is feasible to predict asphalt pavement performance in China.

scholarly journals Assessing the Impact of Enhanced Hydrological Processes on Urban Hydrometeorology with Application to Two Cities in Contrasting Climates

2016 ◽  
Vol 17 (4) ◽  
pp. 1031-1047 ◽  
Author(s):  
Jiachuan Yang ◽  
Zhi-Hua Wang ◽  
Matei Georgescu ◽  
Fei Chen ◽  
Mukul Tewari
Keyword(s):  
Water Cycle ◽  
Wrf Model ◽  
Single Layer ◽  
Urban Canopy ◽  
Field Measurements ◽  
Green Roofs ◽  
The United States ◽  
Climatic Conditions ◽  
Hydrological Processes ◽  
The Impact

Abstract To enhance the capability of models in better characterizing the urban water cycle, physical parameterizations of urban hydrological processes have been implemented into the single-layer urban canopy model in the widely used Weather Research and Forecasting (WRF) Model. While the new model has been evaluated offline against field measurements at various cities, its performance in online settings via coupling to atmospheric dynamics requires further examination. In this study, the impact of urban hydrological processes on regional hydrometeorology of the fully integrated WRF–urban modeling system for two major cities in the United States, namely, Phoenix and Houston, is assessed. Results show that including hydrological processes improves prediction of the 2-m dewpoint temperature, an indicative measure of coupled thermal and hydrological processes. The implementation of green roof systems as an urban mitigation strategy is then tested at the annual scale. The reduction of environmental temperature and increase of humidity by green roofs indicate strong diurnal and seasonal variations and are significantly affected by geographical and climatic conditions. Comparison with offline studies reveals that land–atmosphere interactions play a crucial role in determining the effect of green roofs.

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