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Author: Adrain Reed Gibby
Publisher:
ISBN:
Category :
Languages : en
Pages : 380
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Book Description
Author: Adrain Reed Gibby
Publisher:
ISBN:
Category :
Languages : en
Pages : 380
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Author: A. Reed Gibby
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Category :
Languages : en
Pages : 0
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Category : Highway departments
Languages : en
Pages : 56
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Author: Hilal Abdallah Said Saadi
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Category :
Languages : en
Pages : 388
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Category : Pavements, Asphalt
Languages : en
Pages : 306
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Author: Arudi Rajagopal
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Category : Pavements
Languages : en
Pages : 48
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Book Description
This report presents the details of a study conducted to develop pavement performance prediction models and decision trees for various families of pavements, using the data available with the City of Cincinnati. Required data was acquired from city's pavement inventory database. The road network was divided into two classifications namely, major roads and minor roads. These roads were further grouped based on their structural makeup. Statistical regression models were developed for each group. A decision tree was developed to suggest appropriate maintenance and rehabilitation activities based on the condition of the pavement. The city engineers can use these models in conjunction with their pavement management system to predict the future condition of the highway network in Cincinnati and to implement cost effective pavement management solutions. Using the methodology developed in this study, the engineers can also further improve the accuracy of the models in the future.
Author: Wael Elias Tabara
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Category :
Languages : en
Pages : 0
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Keeping asphalt-surfaced highways and roads in an acceptable condition is the major goal that departments of transportation and pavement engineers always strive to achieve. According to ASCE 2009 report card, an estimated spending of $186 billion is needed annually to substantially improve highways conditions. Hence, prediction models of current and future pavement condition should be rationalized and studied from cost effective perspective. In modeling the pavement condition, two major categories of models have been used: (1) deterministic and (2) stochastic. Existing models consider some factors that might be more critical than others, such as roughness measurements and distress information. They ignore other factors that could have a real effect on the accuracy of the pavement performance model(s), such as climate conditions. Therefore, the current research aims at developing a comprehensive condition-rating model that incorporates a wider range of possible factors significantly affecting flexible pavement performance. Data for this research were collected from the records of Nebraska Department of Roads (NDOR) called "Tab Files". In addition to a questionnaire that was designed and sent to pavement engineers and experts in North America. An integrated model was developed using Multi-Attribute Utility Theory (MAUT) and multiple regression analysis. Sensitivity analysis of the developed regression models is done using Monte-Carlo simulation to quickly identify the high-impact factors. Models' validation shows robust results with an average validity percent of 94% in which they can be utilized by Departments of Transportation (DOT) and/or Pavement Management Systems (PMS) as a useful tool for assessing and predicting pavement conditions.
Author: Paulo Pereira
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ISBN:
Category :
Languages : en
Pages : 3
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Author: Y. Richard Kim
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Category : Highway engineering
Languages : en
Pages : 234
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In an effort to move toward pavement designs that employ mechanistic principles, the AASHTO Joint Task Force on Pavements initiated an effort in 1996 to develop an improved pavement design guide. The project called for the development of a design guide that employs existing state-of-the-practice mechanistic-based models and design procedures. The product of this initiative became available in 2004 in the form of software called the Mechanistic-Empirical Pavement Design Guide (MEPDG). The performance prediction models in the MEPDG were calibrated and validated using performance data measured from hundreds of pavement sections across the United States. However, these nationally calibrated performance models in the MEPDG do not necessarily reflect local materials, local construction practices, and local traffic characteristics. Therefore, in order to produce accurate pavement designs for the State of North Carolina, the MEPDG distress prediction models must be recalibrated using local materials, traffic, and environmental data. The North Carolina Department of Transportation (NCDOT) has decided to adopt the MEPDG for future pavement design work and has awarded a series of research projects to North Carolina State University. The primary objective of this study is to calibrate the MEPDG performance prediction models for local materials and conditions using the data and findings generated from this series of research projects. The work presented in this report focuses on four major topics: (1) the development of a GIS-based methodology to enable the extraction of local subgrade soils data from a national soils database; (2) the rutting and fatigue cracking performance characterization of twelve asphalt mixtures commonly used in North Carolina; (3) the characterization of local North Carolina traffic; and (4) calibration of the flexible pavement distress prediction models in the MEPDG to reflect local materials and conditions.
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Category :
Languages : en
Pages :
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The 1993 American Association of State Highway and Transportation Officials (AASHTO) Guide for Design of Pavement Structures is a mere modification of the empirical methods found in its earlier versions that are based on regression equations relating simple material and traffic inputs. Although the various editions of the AASHTO design guide have served well for several decades, they contain too many limitations to be continued as the nation's primary pavement design procedures. The Mechanistic-Empirical Pavement Design Guide (MEPDG) procedure, on the other hand, provides the tools for evaluating the effect of variations in input data on pavement performance. The design method in the MEPDG is mechanistic because it uses stresses and strains in a pavement system calculated from the pavement response model to predict the performance of the pavement. The empirical nature of the design method stems from the fact that the pavement performance predicted from laboratory-developed performance models is adjusted based on the observed performance from the field to reflect the differences between predicted and actual field performance. The performance models used in the MEPDG are calibrated using limited national databases and, thus, it is necessary to calibrate these models for local highway agencies implementation by taking into account local materials, traffic information, and environmental conditions. Two distress models, permanent deformation and bottom-up fatigue cracking (hereafter referred to as alligator cracking), were employed for this effort. Fifty-three pavement sections were selected for the calibration and validation process: 30 long-term pavement performance (LTPP) pavements, which include 16 new flexible pavement sections and 14 rehabilitated sections, and 23 North Carolina Department of Transportation (NCDOT) sections. All the necessary data were obtained from the LTPP and the NCDOT databases. To provide reasonable values in cases where data were missing, MEP.
