U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590
Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
University students and professors were invited to participate in the 2003 International Contest on LTPP Data Analysis, co-sponsored by the American Society of Civil Engineers (ASCE) and the Federal Highway Administration (FHWA). The contest received 20 papers. It was designed to encourage students and professors from around the world to get involved in using the LTPP database. The contest created an opportunity for students and professors to use this significant database for research, class projects, Master's and Doctoral theses, and practical fieldwork to resolve common engineering problems. This year, ASCE/LTPP received papers in all categories
Winners of this years contest received their awards on Sunday January, 11, 2004 during the LTPP State Coordinators Meeting.
The people photographed are, from left to right, T. Paul Teng, Kumares C. Sinha, James T. Smith, Susan L. Tighe, Vankatesa Prasanna Kumar Ganesan, and Ricardo Oliviera de Sousa.
The papers were evaluated using the following criteria:
Category 1, Undergraduate Students (Individual or team entry) Teams consisted of up to three undergraduate students. The principal author was a student who primarily conducted the analysis.
Category 2, Graduate Students (Individual or team entry) Teams were formed of up to three students, including undergraduate students. The principal author was the graduate student who primarily conducted the analysis.
Category 3, Partnership This category was for undergraduate or graduate students working in partnership with a state highway agency and/or private organization/industry. The teams consisted of up to three students, not including partners. The principal author was the student who primarily conducted the analysis.
Category 4, Curriculum This category was designed to encourage college/university professors to develop an appropriate curriculum using the LTPP database.
|1st Place||Mark P. MacDonald||Larry C. Crowley
Rod E. Turochy
|1st Place||James T. Smith||Susan L. Tighe|
|2nd Place||Ricardo Oliviera de Souza
Silvrano Dantas Neto
|Marcio Muniz de Farias|
|1st Place||Venkatesa Prasanna
|1st Place||Susan L. Tighe|
ASSESSMENT OF OVERLAY ROUGHNESS IN THE LTPP:
A CANADIAN CASE STUDY
James T. Smith
First Place,UNDERGRADUATE CATEGORY
This paper studies asphalt pavement overlay performance in the Canadian environment. It investigates the impact of asphalt overlay thickness, climatic zone, and subgrade type on the progression of roughness as described by the International Roughness Index (IRI). Data from the Canadian Long Term Pavement Performance Test Sites (LTPP) was analyzed. Through the investigation, pavement factors that significantly impact overlay performance in the Canadian environment can be identified.
Data collected over the first 13 years of study were used to show national and provincial roughness trends from 53 test sites. The IRI data was statistically summarized (mean, standard deviation) for each category by the age of the overlay section. Using the summarized data, regression analysis was used to determine an equation that best describes the progression of roughness. Two-factor analysis of variance was used to determine if there were any significant differences within specific categories. The results of the regression analysis were compared to the Canadian Strategic Highway Research Project (C-SHRP) LTPP to confirm the validity of the roughness progression equations.
Results show that overlay thickness and climatic zones significantly impact the roughness, while subgrade type has little influence on the IRI values. The roughness progression equations achieved squared correlation coefficients (R2) between 0.93 and 0.39 demonstrating the accuracy of the model equations.
STATISTICAL ANALYSIS BETWEEN ROUGHNESS INDICES AND ROUGHNESS PREDICTION MODEL USING NEURAL NETWORKS
Ricardo Oliveira de Souza
Silvrano Dantas Neto
Second Place, UNDERGRADUATE CATEGORY
This paper presents an analysis between the International Roughness Index (IRI) and the Standard Deviation of longitudinal roughness (s), as well as a neural network study developed to predict the critical level of roughness. Measured longitudinal profiles available in the Long-Term Pavement Performance (LTPP) program database were used. A total of 207 pavement sections, in 42 States of the USA, were used to do this analysis. Using a suitable software, the International Roughness Index (IRI) and the Standard Deviation of longitudinal roughness (s) values were computed for every longitudinal pavement profile measured. Afterwards, these values were used in regression analysis and it was found a high correlation between them (R2=0.93). Neural networks analysis correlated the IRI computed values with the type of subgrade soil, pavement structure (layer thickness), climate and traffic data of 157 pavement sections. The neural network could forecast the IRI with an extremely high correlation factor (R2=0.99). Besides, neural network provided a sensitivity analysis indicating the relative contribution of factors related to the structural number (49%), climate (31%) and traffic (20%). Multivariate linear and non-linear statistic regressions were also performed in order to predict IRI but they could not find any correlation at all.
INVESTIGATION OF SEASONAL VARIATION IN PAVEMENT FRICTION USING THE DATAPAVE 3.0 DATABASE
Mark P. McDonald
First Place, GRADUATE CATEGORY
The nature of seasonal variations in highway skid resistance is investigated through use of the Datapave 3.0 friction data. The investigation is approached using the first principle of the conservation of energy. Two common theories explaining seasonal variation in skid resistance are considered, one stating that the seasonal variations in skid resistance occur as snowfall removal operations increase microtexture, which is then worn away throughout the summer. The other states that seasonal variations are caused by seasonal differences in pavement temperature.
This research also demonstrates a methodology, which can be adapted to analyze general multi variate statistical systems, and specifically the Datapave pavement structural data. The study utilizes visually-oriented observational study techniques to assess the validity of the hypothesized factor structure in the data and to express it visually. After the visual analysis, structural equations modeling is used to express the structure numerically. The results indicate that snowfall did not organize the data; however, temperature did begin to organize the data. This suggests a temperature effect upon skid resistance and indicates seasonal variations in pavement friction to be dependent upon factors not related to surface texture.
Using the developed model, monthly adjustments for skid numbers were calculated and compared with those currently used by the Virginia Department of Transportation. This comparison showed significant agreement between the developed model and in-place practices.
USE OF LTPP DATA TO VERIFY THE ACCEPTANCE LIMITS DEVELOPED FOR PENNDOT PAVEMENT DISTRESS DATA
First Place, PARTNERSHIP CATEGORY
State transportation agencies utilize various methods of pavement data collection. Manual, film-based, semi-automated and automated are the major methods used by State transportation agencies. The Federal Highway Administration (FHWA) Long-Term Pavement Performance (LTPP) program has utilized both the manual method and the "PADIAS" film-based survey for their pavement data collection. The Pennsylvania Department of Transportation (PENNDOT) replaced their former manual method with a semi-automated method. The project team at the Pennsylvania Transportation Institute developed a quality assurance plan for PENNDOT for pavement data collection and rating. Initial acceptance limits were developed by the project team with the assistance of PENNDOT. The manual distress data are compared with the Pavement Distress Analysis System (PADIAS42) distress data. This paper also summarizes the PENNDOT QA plan. The sources of variability affecting surface distress are also discussed. In this paper, the LTPP distress data are used to verify the PENNDOT acceptance limits. The findings indicate that the proposed limits may require modification. Two types of modifications are attempted with the LTPP data, providing input to PENNDOT's future decisions.
USING LTPP FOR EDUCATING TOMORROW'S ENGINEER
Susan L. Tighe, Ph.D., P.Eng
First Place, CURRICULUM CATEGORY
The overall scope of this paper involves a university perspective on how the LTPP program can be used to educate and train skilled engineers in the pavement sector.
This paper builds on an earlier presentation to the 2003 Transportation Research Board Annual meeting by first presenting a context for using the LTPP data. In formulating and addressing the use of the data, the following main points are discussed: education and training using LTPP, development of assignments with purpose, discussion of using LTPP to develop pavement research themes and conclusions. The paper is primarily directed at academics. However, it does have relevance to the public and private sector as it directs assignments that will result in highly qualified people and potential leaders in the field of pavement engineering. It also recognizes the competing demands that face academics so the assignments are intended to be straight forward and are designed for limited preparation time. Overall there is a need to produce "intelligent" engineers with good problem solving skills. Thus, the primary focus is to encourage independence and creativity through inquiry-based learning.
In summary, this paper has as its basic premise that good design, construction and maintenance of long-life pavements can be most effectively realized in education and training through inquiry-based learning with LTPP.
|»||Office of Infrastructure R&D|
|»||Infrastructure R&D Program|
Performance Program »Foundation
|»||Infrastructure R&D Experts|
|»||Infrastructure R&D Laboratories|
|»||Infrastructure R&D Projects|
|»||Infrastructure R&D Publications|
|»||Infrastructure R&D Topics|
|»||Contact LTPP Customer Support Service Center at firstname.lastname@example.org if you would like to be added to our email distribution list for LTPP Newsletters and other announcements.|