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Author: Lynn D. Evans Publisher: ISBN: Category : Pavements Languages : en Pages : 184
Book Description
The Long Term Pavement Performance (LTPP) program has been collecting profile and International Roughness Index (IRI) information from more than 2,062 test sections since 1989 using K.J. Law 690DNC optical sensor Profilometers. Analysis of the IRI data has been limited, but with the increasing distribution of the LTPP DataPave software, this data is seeing increasing use. In an effort to confirm the quality of LTPP IRI data in the Information Management System (IMS) database and to document its variability, LTPP initiated an analysis of IRI variability in September 1997. This report documents the results of that study.
Author: Lynn D. Evans Publisher: ISBN: Category : Pavements Languages : en Pages : 184
Book Description
The Long Term Pavement Performance (LTPP) program has been collecting profile and International Roughness Index (IRI) information from more than 2,062 test sections since 1989 using K.J. Law 690DNC optical sensor Profilometers. Analysis of the IRI data has been limited, but with the increasing distribution of the LTPP DataPave software, this data is seeing increasing use. In an effort to confirm the quality of LTPP IRI data in the Information Management System (IMS) database and to document its variability, LTPP initiated an analysis of IRI variability in September 1997. This report documents the results of that study.
Author: Kenneth H. McGhee Publisher: Transportation Research Board ISBN: 0309070120 Category : Automatic data collection systems Languages : en Pages : 94
Book Description
At head of title: National Cooperative Highway Research Program.
Author: Publisher: ISBN: Category : Pavements Languages : en Pages : 152
Book Description
Reliable distress data for pavement performance model development and validation, and other pavement engineering products, are critical to the success of the Long-Term Pavement Performance (LTPP) program. Confidence in distress data requires a measure of error because of the bias and precision components of its variability. No systematic evaluation has been performed to quantify the bias and variability associated with both the manual and PASCO film-based distress data. In view of this, this study was undertaken by the Federal Highway Administration (FHWA) to assess the variability of the LTPP distress data, including those in the Information Management System (IMS) and those currently being collected using either photographic or manual methods.
Author: Gonzalo Rada Publisher: ISBN: Category : Pavements Languages : en Pages : 120
Book Description
This report summarizes the LTPP monitoring data collection activities for inclusion in the National Pavement Performance Database. The pavement condition monitoring data include identification of surface distress, profile measurements, deflection testing results, and surface friction measurements. The report also describes traffic, climate, maintenance, rehabilitation, and seasonal monitoring and data collection. Other results and products of the 5-year pavement condition monitoring activity are listed.
Author: Kaan Özbay Publisher: ISBN: Category : Pavements Languages : en Pages : 152
Book Description
The significant contribution of the research presented in this report lies in the fact that it utilizes the most comprehensive database of pavement conditions that is readily available and promises to provide the sought data in future years. The first part of this report reviews the existing literature covering related topics including pavement roughness, the LLTP background, artificial neural networks, regression analysis and the existing pavement deterioration models. The second part discusses the work done in data analysis and data manipulation in addition to the development of the training of the neural network model. The third part deals with various aspects of the model development using neural networks and regression analysis. The next part concludes the research with summarizing the results of model development. The models developed in this research are then compared to some existing models by applying the models to similar data sets and performing statistical analysis of the results.
Author: Lynn D. Evans
Author: Lynn D. Evans
Author: Kenneth H. McGhee
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Author: Rohan W. Perera
Author: Gonzalo Rada
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Author: Kaan Özbay
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