scholarly journals Development of pavement performance prediction model for bituminous hot mix asphalt on interurban road networks

2017 ◽  
Vol 23 (6) ◽  
pp. 718-725
Author(s):  
Ufuk Kırbaş ◽  
Mustafa Karaşahin ◽  
Emine Nazan Ünal ◽  
Muhammet Komut ◽  
Birol Demir ◽  
...  
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Hot Mix Asphalt ◽  
Road Networks ◽  
Pavement Performance ◽  
Pavement Performance Prediction

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.

Development of Performance-Related Specifications for Hot-Mix Asphalt Pavements Through WesTrack

1997 ◽  
Vol 1575 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Stephen B. Seeds ◽  
Rudramunniyappa Basavaraju ◽  
Jon A. Epps ◽  
Richard M. Weed
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Permanent Deformation ◽  
Fatigue Cracking ◽  
Primary Objective ◽  
Pavement Performance ◽  
Void Content ◽  
Target Values ◽  
Pavement Performance Prediction

The primary objective of the FHWA-sponsored WesTrack project is to further the development of performance-related specifications for hotmix asphalt construction. This objective is being achieved, in part, through the accelerated loading of a full-scale test track facility in northern Nevada. Twenty-six hot-mix asphalt test sections constructed to meet the criteria set forth in a statistically based experiment design are providing performance data that will be used to improve existing (or develop new) pavement performance prediction relationships that better account for the effects that “off-target” values of asphalt content, air-void content, and aggregate gradation have on such distress factors as fatigue cracking, permanent deformation, roughness, raveling, and tirepavement friction. The concept of the planned new performance-related specification and how it will incorporate the modified pavement performance prediction models are described. The current plan for assessing contractor pay adjustments (i.e., penalties and bonuses) based on data collected from the as-constructed pavement is also discussed.

Pavement Performance Prediction Model Based on Weibull Distribution

2013 ◽  
Vol 378 ◽  
pp. 61-64 ◽  
Author(s):  
Ting Peng ◽  
Xiao Ling Wang ◽  
Shuan Fa Chen
Keyword(s):  
Weibull Distribution ◽  
Prediction Model ◽  
Failure Analysis ◽  
Performance Prediction ◽  
Performance Data ◽  
Experimental Results ◽  
Pavement Performance ◽  
Ideal Model ◽  
Practical Performance ◽  
Pavement Performance Prediction

The Weibull distribution is an ideal model for failure analysis. In this work, it is applied to simulate pavement performance regression process. Then, pavement performance prediction model is constructed according to the Weibull distribution. Historical pavement performance data are used to evaluate the practical performance of the model. According to the experimental results, ideal performance is obtained. It provides more accurate results compared with the previous work.

scholarly journals Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Gang Yu ◽  
Shuang Zhang ◽  
Min Hu ◽  
Y. Ken Wang
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Performance Prediction ◽  
Pavement Performance ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Digital Twin ◽  
Highway Tunnel ◽  
The Mean ◽  
Pavement Performance Prediction

The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

Pavement Management Systems: Past, Present, and Future

2003 ◽  
Vol 1853 (1) ◽  
pp. 65-71 ◽  
Author(s):  
Ram B. Kulkarni ◽  
Richard W. Miller
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Systems ◽  
Seamless Integration ◽  
Positioning Systems ◽  
Pavement Management Systems ◽  
Transportation Organization ◽  
Pavement Performance Prediction

The progress made over the past three decades in the key elements of pavement management systems was evaluated, and the significant improvements expected over the next 10 years were projected. Eight specific elements of a pavement management system were addressed: functions, data collection and management, pavement performance prediction, economic analysis, priority evaluation, optimization, institutional issues, and information technology. Among the significant improvements expected in pavement management systems in the next decade are improved linkage among, and better access to, databases; systematic updating of pavement performance prediction models by using data from ongoing pavement condition surveys; seamless integration of the multiple management systems of interest to a transportation organization; greater use of geographic information and Global Positioning Systems; increasing use of imaging and scanning and automatic interpretation technologies; and extensive use of formal optimization methods to make the best use of limited resources.

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