Tyler Dawson

Backcalculated and Laboratory-Measured Resilient Modulus Values

The resilient modulus (MR) of roadbed soils is a necessary parameter in pavement design. The MR of roadbed soils is dependent on the soil type, water content, dry density, particle gradation, Atterberg limits, and stress states. Several procedures can be used for the determination of the MR: laboratory testing, backcalculation with nondestructive deflection testing (NDT) data, and correlations to other soil parameters such as the California bearing ratio, density, and water content. The first procedure is time-consuming and expensive and requires substantial resources to cover the various roadbed soils under the road network. The second is relatively inexpensive and fast and can be designed to cover representative soils under the pavement network. The third procedure is approximate and may be used for the Level 2 design of the Mechanistic–Empirical Pavement Design Guide. The Michigan Department of Transportation (MDOT) sponsored a research project for the development of a systematic procedure to determine the MR values for all soil types encountered under the pavement network in the state of Michigan. The procedure includes laboratory testing of representative disturbed and undisturbed soil samples, NDT with the MDOT falling weight deflectometer, and developing correlations between the MR values obtained from the two tests and between the MR values and other soil parameters that can be obtained using simple tests. The developed correlation equations between the MR measured values and the other soil parameters are presented elsewhere, whereas the correlations between the laboratory-obtained and the backcalculated MR values are discussed in this paper.

Global procedure for temperature adjustment of measured pavement deflection data: Based on the long-term pavement performance Seasonal Monitoring Program

Pavement surface deflection testing is carried out by most state highway agencies (SHAs) on a limited basis to assess the structural capacity of existing pavements and to select and design pavement treatments. One difficulty with analyzing pavement deflection data is that, for flexible pavements, the deflection is dependent on temperature. To properly use the deflection data, the data should be adjusted on the basis of the measured temperature during the data collection process. A few temperature adjustment procedures have been developed in the past, but most cannot be used globally and for all scenarios and they require input data that are generally not collected by most SHAs. This paper details a simplified process that can be easily used by SHAs to adjust the measured pavement deflection to a 21°C testing scenario, with data they routinely collect during falling weight deflectometer testing, to design pavement treatments such as asphalt overlay. During this study, sponsored by the Long-Term Pavement Performance (LTPP) program, the LTPP database was used to develop a global procedure to adjust the measured pavement deflection to the standard testing temperature of 21°C. Most of the LTPP test sections have been subjected to deflection testing on a periodic basis; test sections included in the LTPP Seasonal Monitoring Program were subjected to frequent deflection testing over time. The 41 test sections included in the analyses had different pavement cross sections, were subjected to various maintenance activities, were located in four climatic regions, and were subjected to various traffic levels. This robust deflection database is representative of the various SHA pavement networks across North America. The results and the processes can be adopted to meet the needs of most SHAs.

Impact of three state practices on effectiveness of hot-mix asphalt overlay

The effectiveness of pavement treatments is a function of several factors, including material quality, design, distribution of pavement condition states (conditions and rates of deterioration) before treatment, and construction quality. Because of these factors, state highway agencies have established estimates of treatment service lives with significantly wide ranges. These wide ranges make the calculation of treatment benefits or effectiveness a difficult task unless the role of each factor that affects service life is well understood. This study analyzed the impacts of the state of the practice of Colorado, Louisiana, and Washington on the effectiveness of thin (< 2.5 in.) hot-mix asphalt (HMA) overlay treatment. This paper reports that analysis of time-series pavement condition and distress data of previously treated pavement sections can be tabulated in a matrix format to demonstrate a snapshot examination of past practice. Such matrices are called treatment transition matrices (T2Ms). The data in the matrices express the probability that a pavement treatment will have certain effectiveness. T2Ms for pavement projects subjected to thin HMA overlays by three state highway agencies are discussed. Differences in treatment effectiveness were related to differences in the state of the practice in the three states. Selection of treatment timing and project boundaries significantly affected treatment effectiveness, and similar states of the practice yielded similar treatment effectiveness.

Defining Benefits from Pavement Rehabilitation and Preservation

Pavement service life can be defined as the estimated number of years between pavement construction or rehabilitation and when the pavement section reaches a given condition (rutting, cracking, roughness, etc.) threshold value. At that time, the pavement section is typically subjected to rehabilitation or preservation actions. The two overarching methodologies for determination of pavement fix benefits are: 1) the extension in the pavement service life due to a given fix and 2) the calculation of the area between the pavement performance curves and a given threshold value. The life extension method places importance in prolonging the service life of the pavement. On the other hand, the area under the performance curve method places importance on the level of pavement distress over time. Selection of the pavement fix type and time with the lowest cost to benefit ratio could be considered “optimal”.

Effective Systems for Rating Pavement Condition

Existing systems for rating pavement condition are primarily based on current pavement conditions, distresses, or both. These systems do not account for the pavements deterioration rates. For instance, two pavement sections rated fair this year may or may not have similar ratings next year. A balanced and comprehensive system for rating pavement condition should be based on the pavements conditions, distresses, and deterioration rates. In a research study sponsored by FHWA, dual systems of rating pavement condition were developed on the basis of the pavements functional and structural aspects and rates of deterioration. These rating systems account for current and future pavement conditions and distresses. Therefore, the systems provide more accurate information to decision makers about management of the pavement network while being simple enough for communication with legislators and the general public. The functional rating is based on ride quality (international roughness index) and safety (skid resistance and rutting) and is expressed by the remaining functional period (RFP). The structural rating is based on cracking, faulting, and rutting and is expressed by the remaining structural period (RSP). The RFP is defined by the shortest time in years between now and the year when one of the conditions reaches its threshold value. Similarly, the RSP is the shortest period in years between now and when a threshold is reached. The main advantage of the RFP and RSP is that both are proportional to the elapsed time. Their values decrease by 1 year for each calendar year.

Impacts of Climatic Regions and Design Factors on the Condition and Distress of Specific Pavement Test Sections in the Long-Term Pavement Performance Program

One of the objectives of the Long-Term Pavement Performance Specific Pavement Studies (SPS)-1 experiment was to examine the effects of climatic regions and various design factors on the performance of flexible pavements. In a research study sponsored by FHWA’s Long-Term Pavement Performance program, the impacts of climatic regions, drainage, and the thickness of asphalt concrete layers on the functional and structural performance of the flexible pavement test sections of the SPS-1 experiment were analyzed. Functional performance was analyzed on the basis of ride quality [determined by using the international roughness index (IRI) and safety (rut depth)]. Structural performance was analyzed on the basis of alligator, transverse, and longitudinal cracking and rut depth. For each SPS-1 test section, functional performance was represented by the remaining functional period (RFP), and structural performance was represented by the remaining structural period (RSP). Both metrics, the RFP and the RSP, were developed in this study to rate the performance of flexible, rigid, and composite pavement sections. This paper presents and discusses the results of the analyses of the performance of the SPS-1 test sections. It is shown that, as expected, pavement performance is a function of the climatic region. Wet–freeze regions had a significant impact on pavement performance in terms of the IRI, rut depth, and cracking, and drainable bases were found to be more effective in the wet–freeze region than in the wet–no-freeze, dry–freeze, and dry–no-freeze regions.

Modeling Pavement Conditions of Multilane Roads with Measured Driving Lane Data

In the past, highway authorities have selected pavement treatment type on the basis of the driving lane conditions and have applied the same treatment to all lanes. Recently, authorities started applying different treatments to the driving and passing lanes at different times. Because of costs, most highway authorities collect only the driving lane pavement condition data. Therefore, the passing lane pavement conditions need to be modeled with the driving lane data. To address this need, time series pavement condition and distress data along the driving and passing lanes of the Minnesota Road Research Project were obtained. The data were analyzed to develop and verify methodologies to estimate the passing lane conditions and distresses accurately with the use of the measured driving lane data. The methodologies are based on the fact that most pavement distresses are caused by material and environmental factors and by traffic loads. The effects of the first two factors are similar on adjacent driving and passing lanes. The differences in the conditions between adjacent lanes are mainly because of traffic distribution, which is expressed by the lane distribution factor. The developed methodologies use the measured pavement conditions along the driving lane and the lane distribution factors to estimate the time series conditions of the passing lane. For asphalt pavements, the time-dependent conditions of the passing lane can be accurately estimated (with low standard errors) with the measured conditions of the driving lane and the lane distribution factors. Procedural steps for implementing the methodologies are included.

The remaining service life a good pavement management tool

The remaining service life (RSL) of a given pavement section is the estimated number of years between now and the time when the pavement conditions reach certain amount of distress. The calculation of RSL is based on two steps; fitting the proper mathematical function to the time series condition data, and predicting the time at which the pavement reaches the threshold conditions. Time series pavement condition data of several miles of roads were obtained from four State Highway Agencies (SHAs). The data were analyzed and the RSL of each 0.1-mile section of each road was calculated based on the International Roughness Index, (IRI), rut depth and cracking data. Results of the analyses are presented in this paper. It is shown that, the RSL is a good communicating and strategy planning tool. It could be used to address engineers, the public, legislators and managers.

The Calculation of the Remaining Service Life Based on the Pavement Distress Data

The distress points, which are the numerical values assigned to pavement distress are not universal or standardized. Most agencies assign these points based on past experience with the intention to periodically calibrate them. The distress points significantly affect the distress indices, the life cycle cost, the remaining service life (RSL), and the accuracy of the pavement decisions. Distress data were obtained from four State Departments of Transportation. The data were analyzed and the RSL was calculated and equalized to the dollar values of the network and its rate of depreciation. Results of the analyses are presented in this paper. It is shown that the RSL could be calculated without the need to assign distress points. It is also shown that the weighted average RSL of the network reflects the dollar value of the network and its rate of depreciation. The RSL could be used to support accurate pavement decisions.

The Effect of Pavement Condition Data Sampling on Project Boundary Selection

Pavement condition data are either continuously collected or sampled. In sampling it is assumed that the time and costs of data collection can be reduced and the pavement condition of shorter segments represent the conditions of larger sections. In a study sponsored by the Federal Highway Administration, pavement distress data along several miles of roads were obtained from four State Highway Agencies. Each department collects and stores distress data on a continuous basis for each 0.1 mile section along the network. The continuous data were sampled and the impacts on the accuracy of pavement decisions were analyzed. Results of the analyses are discussed herein. It is shown that, for variable pavement conditions, ten percent sampling leads to inaccurate decisions. Pavement sections in need of repair are ignored whereas healthy sections are selected for repair. The costs incurred due to inaccurate decisions could be much higher than the saving incurred by sampling.