Validation of Long-Term Pavement Performance Prediction Models for Resilient Modulus of Unbound Granular Materials

2016 ◽  
Vol 2578 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Haithem Soliman ◽  
Ahmed Shalaby
Keyword(s):  
Granular Materials ◽  
Performance Prediction ◽  
Prediction Models ◽  
Resilient Modulus ◽  
Pavement Performance ◽  
Unbound Granular Materials ◽  
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.

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.

scholarly journals Comparisons of Faulting-Based Pavement Performance Prediction Models

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Weina Wang ◽  
Yu Qin ◽  
Xiaofei Li ◽  
Di Wang ◽  
Huiqiang Chen
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Pavement Performance ◽  
Physical Parameters ◽  
Ann Model ◽  
Climate Data ◽  
Good Tool ◽  
Advantages And Disadvantages ◽  
Lower Accuracy ◽  
Pavement Performance Prediction

Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR) model, artificial neural network (ANN) model, and Markov Chain (MC) model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

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