International Center for Partnered Pavement Preservation (ICP3): First Year Progress Report

0-6878The Accelerating Innovation in Partnered Pavement Preservation project was initiated to promote and streamline research in the area of pavement preservation and to optimize the use of Texas Department of Transportation's (TxDOT\u2019s) research and implementation resources by fostering cooperation and collaboration with the US DOT Center for Highway Pavement Preservation (CHPP). CHPP is a research and innovation partnership lead by Michigan State University which members include: The University of Texas at Austin, The University of Illinois at Urbana-Champaign, The University of Minnesota, The University of Hawaii at Manoa and North Carolina A&T University. This preliminary progress report summarizes the work performed during the first five months of the project, from April to August 2015. During this period two task orders were developed and the corresponding work was planned and initiated. This report also presents the initial findings of these two task orders. The two task orders are: 1) Determination of Field Performance of Thin Overlays Relative to Alternative Preservation Techniques and 2) Quantification of Highway Pavement Surface Micro- and Macro-Texture

Efficient Model for Predicting Friction on Texas Highway Network

0-7031The objective of this project was to develop a model to predict friction that could be applied at the network level to overcome some of the issues associated with friction measuring equipment. This project developed an instrument that can collect high-resolution surface profiles to determine macrotexture and microtexture under different conditions and on different surface types. These data were used to develop a model to predict friction and skid number with a high degree of accuracy. The instrument is able to collect data at highway speed, allowing accurate texture data collection on the entire network on an annual basis, and is small enough to attach to any surveying vehicle, so texture data can be collected as part of other operations, eliminating the need for an independent data collection effort. The development of this instrument provides not only savings but also enhances operational safety. The model was calibrated for 29 pavement sections in the Austin District, so the researchers recommend implementing the findings of this project and extending the calibration of the model to more pavement sections around the state

Development of a Three-Dimensional Laser Scanning Prototype for Pavement Preservation Applications

0-6878The connection between the pavement friction and surface macro- and micro-texture makes pavement texture a vital topic for highway agencies and state Departments of Transportation to address. Accordingly, measuring surface texture is of prime importance in pavement preservation applications. Currently, most highway agencies rely on subjective measuring methods of pavement texture that are not reliable. With the recent advancements in laser technologies, highway agencies are gearing towards employing such developments that could potentially contribute towards better pavement surface texture characterization. With better surface texture characterization, they can better assess, monitor, and improve the pavement texture to provide better skid resistance for their highway network with the aim of ensuring safer roads for the public. This report proposes a laser scanning prototype for the characterization of the micro- and macro-texture of road surfaces. This prototype system has the advantage of capturing 3D data on pavement surfaces using an automatic, simple, and quick operation. This prototype is able to work in laboratory and in field. The development stages of this prototype are provided in this report. In addition, this report discusses different applications of the prototype created. The findings of this research are provided in three parts. Part I involves the feasibility of the prototype in characterizing the micro-texture of aggregates. Part II involves developing an algorithm to measure the mean texture depth of pavement surfaces. Part III involves finding the connection between the pavement friction and texture. Other applications of this prototype are in chip seal design. The results study and findings regarding the chip seal design are provided in the appendix

Modeling Pavement Performance by Combining Field and Experimental Data

The accurate prediction of pavement performance is important for efficient management of the surface transportation infrastructure. By reducing the error of the pavement deterioration prediction, agencies can obtain significant budget savings through timely intervention and accurate planning.The goal of this research was to develop a methodology for developing accurate pavement deterioration models to be used primarily for the management of the road infrastructure. The loss of the riding quality of the pavement was selected as the performance indicator. Two measures of riding quality were used: serviceability (Present Serviceability Index, PSI) and roughness (International Roughness Index, IRI).An acceptable riding quality is important for both the road user and the goods being transported. Riding quality affects the comfort of the user for whom the road is provided, and the smoothness with which goods are moved from one point to another. The vehicle operating costs and the costs of transporting goods increase as the road riding quality deteriorates. These costs are often one order of magnitude more important than the cost of maintaining the road to an acceptable level of service.The initial incremental models developed in this dissertation predict serviceability as a function of material properties, pavement structural characteristics, traffic axle configuration, axle load, and environmental variables. These models were developed applying nonlinear estimation techniques using an experimental unbalanced panel data set (AASHO Road Test). The unobserved heterogeneity among the pavement sections was accounted for by using the random effects approach.The serviceability models were updated using joint estimation with a field panel data set (MnRoad Project). The updated model estimates riding quality in terms of roughness. This was possible by applying a measurement error model to combine both data sources.The main contribution of this research is not the development of a deterioration model itself, but rather the demonstration of the feasibility of using joint estimation and its many advantages, such as: (i) identification and quantification of new variables, (ii) efficient parameter estimates, (iii) bias identification and correction, and (iv) use of a measurement error model to combine apparently incompatible data sources

A New Approach for Allocating Highway Costs

The allocation of highway costs is constantly debated among legislatures, highway agencies, and highway users as it directly relates to concerns about equity in terms of cost responsibility and actual user charges. One of the major challenges in highway cost allocation stems from the need to estimate pavement damage by different vehicle classes. Normally, the calculation of damage caused by heavy vehicles to the highway infrastructure utilizes the concept of Equivalent Single Axle Load (ESAL). This concept was empirically established after the American Association of State Highway Officials America (AASHO) Road Test almost half a century ago. Although the ESAL concept is widely used in pavement design, it has a number of shortcomings when applied for the estimation of pavement damage by different vehicle classes. Some of these limitations include: failure to account for specific infrastructure and environmental conditions, disregard of the differences in traffic configurations and composition, and the inability to capture different distress types. This leads to a fairly inaccurate and generic estimation of pavement damage by vehicle class. This paper proposes an innovative and more rational highway cost allocation approach based on the recently completed guide for the "Mechanistic-Empirical Design Guide of New and Rehabilitated Pavement Structures" developed under the National Cooperative Highway Research Program (NCHRP) Project 1-37A. The Guide accounts for all factors that contribute to pavement deterioration, thereby addressing the shortcomings of an ESAL-based analysis listed earlier. Estimates for pavement damage attributable to each vehicle class can thus be accurately simulated. For the purposes of this study, traffic data collected at a weigh-in-motion station in Texas were used to estimate the highway cost shares of different vehicle classes, given different pavement structural capacities

A New Approach for Allocating Highway Costs

The allocation of highway costs is constantly debated among legislatures, highway agencies, and highway users as it directly relates to concerns about equity in terms of cost responsibility and actual user charges. One of the major challenges in highway cost allocation stems from the need to estimate pavement damage by different vehicle classes. Normally, the calculation of damage caused by heavy vehicles to the highway infrastructure utilizes the concept of Equivalent Single Axle Load (ESAL). This concept was empirically established after the American Association of State Highway Officials America (AASHO) Road Test almost half a century ago. Although the ESAL concept is widely used in pavement design, it has a number of shortcomings when applied for the estimation of pavement damage by different vehicle classes. Some of these limitations include: failure to account for specific infrastructure and environmental conditions, disregard of the differences in traffic configurations and composition, and the inability to capture different distress types. This leads to a fairly inaccurate and generic estimation of pavement damage by vehicle class. This paper proposes an innovative and more rational highway cost allocation approach based on the recently completed guide for the “Mechanistic-Empirical Design Guide of New and Rehabilitated Pavement Structures” developed under the National Cooperative Highway Research Program (NCHRP) Project 1-37A. The Guide accounts for all factors that contribute to pavement deterioration, thereby addressing the shortcomings of an ESAL-based analysis listed earlier. Estimates for pavement damage attributable to each vehicle class can thus be accurately simulated. For the purposes of this study, traffic data collected at a weigh-in-motion station in Texas were used to estimate the highway cost shares of different vehicle classes, given different pavement structural capacities