VB on the Highway Pavement Performance Prediction and Optimization of Maintenance Programs

2011 ◽  
Vol 90-93 ◽  
pp. 2015-2019
Author(s):  
Pei Feng Cheng ◽  
Qian Qian Zhao
Keyword(s):  
Original Data ◽  
Economic Benefits ◽  
Pavement Performance ◽  
Dynamic Programming Method ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Module Prediction ◽  
Evaluation Module ◽  
Pavement Performance Prediction ◽  
Maintenance Plan

Highway maintenance is the main new task after highway construction. In order to predict pavement performance reasonably, and choose the most reasonable maintenance plan, based on investigating and inspecting the pavement condition , use computer language of VB from inputting the data module, establishing evaluation module, maintenance module, prediction module, economic evaluation module and statements processing module of pavement maintenance management system. Put the original data into this system. The system can predict pavement performance and choose maintenance scheme automatically, and use multi-objective optimization model, the effect of the definition and dynamic programming method to optimize the scientific and reasonable maintenance plan, which is from technical and economic aspects to realize by using the limited maintenance funds to achieve the best maintenance effect and to obtain the biggest social and economic benefits.

The Application of Extension Theory on Pavement Performance Evaluation

2010 ◽  
Vol 168-170 ◽  
pp. 111-115
Author(s):  
Cheng Ling ◽  
Lan Zhou ◽  
Fan Gu
Keyword(s):  
Performance Evaluation ◽  
Evaluation Model ◽  
Extension Theory ◽  
Pavement Performance ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Model Based ◽  
General Evaluation ◽  
Conservation Plan ◽  
Solid Foundation

Pavement performance evaluation is a reasonable safeguard for the conservation plan and an important basis for investment decisions. In order to obtain a precise and quantitative evaluation result, a general evaluation model based on Extension Theory is established, and a concrete index is raised to indicate the pavement real condition. Practical pavement detection data of Jing-hu highway in Jiangsu Province is used to validate this model. The result shows that the evaluation model based on Extension Theory gives an accurate evaluation on pavement performance, and reflects the pavement condition well. It could provide solid foundation for pavement maintenance orders, and may have a bright future.

scholarly journals Study of Site-Specific Roughness Progression for a Bitumen-Sealed Unbound Granular Pavement Network

2003 ◽  
Vol 1819 (1) ◽  
pp. 273-281 ◽  
Author(s):  
P. D. Hunt ◽  
J. M. Bunker
Keyword(s):  
Road Network ◽  
Network Performance ◽  
Comprehensive Evaluation ◽  
Average Rate ◽  
Pavement Management ◽  
Pavement Performance ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Pavement Management Systems ◽  
Economic Analyses

Pavement management systems assist engineers in the analysis of road network pavement condition data and subsequently provide input to the planning and prioritization of road infrastructure works programs. The data also provide input to a variety of engineering and economic analyses that assist in determining the future road network condition for a range of infrastructure-funding scenarios. The fundamental calculation of future pavement condition is commonly based on a pavement age versus pavement roughness relationship. However, roughness–age relationships commonly do not take into account the pavement’s historical performance; rather, an “average” rate of roughness progression is assigned to each pavement based on its current age or current roughness measurement. Results of a research project are documented; the project involved a comprehensive evaluation of pavement performance by examining roughness progression over time with other related variables. A method of calculating and effectively displaying roughness progression and the effects of pavement maintenance was developed. The method provides a better understanding of pavement performance, which in turn led to a methodology of calculating and reporting road network performance for application to the pavement design and delivery system in Queensland, Australia. Means of using this information to improve the accuracy of roughness progression prediction were also investigated.

North Carolina’s Experience in Development of Pavement Performance Prediction and Modeling

1997 ◽  
Vol 1592 (1) ◽  
pp. 80-88 ◽  
Author(s):  
Paul K. Chan ◽  
Mary C. Oppermann ◽  
Shie-Shin Wu
Keyword(s):  
North Carolina ◽  
Performance Prediction ◽  
Pavement Performance ◽  
Department Of Transportation ◽  
Pavement Condition ◽  
North Carolina Department ◽  
Condition Rating ◽  
The North ◽  
Initial Idea ◽  
Pavement Performance Prediction

Development efforts in pavement performance prediction by the North Carolina Department of Transportation are described. Research into other states’ approaches was also conducted. The initial idea was to use family curves. However, because of a lack of data in key areas, it was decided to use an individual section’s pavement condition rating (PCR) data for performance prediction. The process of selection and justification of a functional form for curve fitting is detailed. An adaptive scheme to accommodate a realistic PCR history containing cycles of decline and improvement in the ratings is detailed. Abnormal sections that did not fit the models developed for individual sections were identified. These were either ( a) section with too few datum points for modeling or ( b) sections in which the last few ratings leveled out, resulting in a prediction of an unreasonably long life span. The development of family curves and their application in the processing of abnormal sections are also discussed. The developed models were then evaluated by comparing the predicted rating with the actual rating.

Update of Long-Term Pavement Performance Manual Distress Data Variability: Bias and Precision

1998 ◽  
Vol 1643 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Gonzalo R. Rada ◽  
Chung L. Wu ◽  
Gary E. Elkins ◽  
Rajesh K. Bhandari ◽  
William Y. Bellinger
Keyword(s):  
Data Collection ◽  
Prediction Models ◽  
Survey Methods ◽  
Quality Data ◽  
Pavement Performance ◽  
Pavement Condition ◽  
Pavement Distress ◽  
Black And White ◽  
Pavement Performance Prediction

Pavement distress surveys based upon field interpretation and manual mapping and recording of the distress information on paper forms has been used in the Long-Term Pavement Performance (LTPP) program to collect important pavement condition and distress data. Although this manual method was used in the past as a backup to the 35-mm black and white photographic-based method, recently the use of manual distress survey methods has increased in intensity and coverage. To promote uniformity and consistency of distress data collection, one of the early LTPP efforts was to develop standard definitions, measurement procedures and data collection forms. Various quality control and quality assurance functions have also been implemented to provide for high quality data. However, despite these efforts, manual surveys are still based upon a single rater’s subjective classification of distresses present in the field. Recognizing that rater variability exists, a study was undertaken by FHWA to assess the level of variability between individual distress raters and to address the potential precision and bias. Results from nine LTPP distress rater-accreditation workshops conducted during the period of 1992 to 1996 were used as the source of data. Analyses of those data led to numerous observations and conclusions regarding the bias and precision of LTPP distress data. Because LTPP distress data are to be used in the development of pavement performance prediction models, it is believed that the level of variability found in this study should be reduced to increase its potential usage in the development of such models. A number of recommendations to improve the variability associated with manual distress surveys data are included.

Network-Level Pavement Performance Prediction Model Incorporating Censored Data

2003 ◽  
Vol 1853 (1) ◽  
pp. 72-79 ◽  
Author(s):  
Rodney R. DeLisle ◽  
Pasquale Sullo ◽  
Dimitri A. Grivas
Keyword(s):  
Censored Data ◽  
Performance Prediction ◽  
Prediction Models ◽  
New York State ◽  
Pavement Performance ◽  
Actual Performance ◽  
Pavement Condition ◽  
Condition Rating ◽  
Performance Curves ◽  
Pavement Performance Prediction

A methodology is presented for network-level pavement performance prediction that incorporates censored condition data. Censoring occurs when the duration at a specific condition level is not completely observed. This happens when pavement condition is improved and for the duration of the latest condition rating on file for each highway section. Pavement condition history files may contain significant quantities of censored data, yet such data typically are excluded when performance curves are developed. As a result, estimated condition durations and corresponding deterioration curves include deterioration rates that are greater, sometimes substantially greater, than those actually observed. The primary purpose for developing the presented methodology is to correct this shortcoming. Methodology development was facilitated with the use of a comprehensive information basis containing up to 20 years of historical pavement condition data for approximately 19,000 highway sections maintained by the New York State Department of Transportation. Durations at each condition rating were determined for each highway section over the 20-year period, with distinctions made between censored and uncensored observations. A modeling approach, with probability plotting and parameter estimation, was developed that resulted in performance curves. Differences in pavement performance based on geographic region were also investigated. From results obtained with the developed methodology, the main conclusion of this study is that accommodating censored data in pavement performance prediction models not only is feasible but better describes actual performance than if the data were simply excluded from the analysis.

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