Jeremy Lea

CalME, a Mechanistic-Empirical Program to Analyze and Design Flexible Pavement Rehabilitation

A computer program known as CalME has been developed for analysis and design of new flexible pavements and rehabilitation of existing pavements. The paper describes the overlay design procedure and the calibration of the models for reflection cracking and permanent deformation through heavy vehicle simulator (HVS) tests. To simplify the input process, the program includes databases for traffic loading, climatic conditions, and standard materials. A companion program was developed for backcalculation of layer moduli, and the results may be automatically imported into the CalME database. The program incorporates the existing, empirical California Department of Transportation design methods as well as an incremental–recursive analysis procedure based on the mechanistic–empirical method. The effects of different pavement preservation and rehabilitation strategies on pavement damage may be studied with several options for triggering timing of placement. The influence of within-project variability on the propagation of damage can be evaluated using Monte Carlo simulation. The program also permits importation of the results of HVS or track tests into the database and simulation of the experiments on the computer. This feature is useful for the calibration of the mechanistic–empirical models but may also be used for in-depth interpretation of accelerated pavement testing results. An HVS experiment that was used for calibration of the reflection cracking and the permanent deformation models is described.

Using spatial statistics to characterise pavement properties

This paper provides an overview of the statistical models available to characterise spatial variability, and some applications of these models to pavements. It is intended as an introduction for pavement engineers, because these methods are not widely known in the pavements community, and as a guide for conducting simple spatial analysis. Spatial statistics offer a number of insights into the nature of variability in pavement materials and structure, such as the range over which one can expect to see correlation between measurements, and provide a statistically sound procedure for estimating properties between samples. These models have been used recently to gain insights into variability in pavement performance, which could lead to them being incorporated into mechanistic-empirical design methods, although more works still need to be done in this area.

Impact of Pavement Roughness on Vehicle Free-Flow Speed

In earlier studies of the environmental impact of pavement roughness on life cycle greenhouse gas (GHG) emissions, it was assumed that pavement roughness (usually measured by International Roughness Index, IRI) has no impact on vehicle speed. However, because ride comfort increases when a pavement becomes smoother (that is, when roughness decreases), it is possible that people will drive faster on a smoother pavement. Because most vehicles achieve maximum fuel efficiency between 40 and 50 mph (64 and 80 km/h), fuel use increases at speeds beyond this range, and this increase in speed might offset the benefits gained from the reduced rolling resistance associated with reduced pavement roughness. Therefore, to investigate the impact of changes in pavement roughness on driving behavior with respect to speed, this study built a linear regression model to estimate free-flow speed on freeways in California. The explanatory variables included lane number, total number of lanes, day of the week, region (Caltrans district), gasoline price, and pavement roughness as measured by IRI. Data from the California freeway network from 2000 to 2011 were used to build the model. The results show that pavement roughness has a very small impact on free-flow speed within the range of this study. For the IRI coverage in this study (90 percent of the records have an IRI of 3 m/km or lower and 90 percent of the records have an IRI change of 2 m/km or lower), a change in IRI of 1 m/km (63 in./mi) resulted in a change of average free-flow speed of about 0.48 to 0.64 km/h (0.3 to 0.4 mph), a value low enough to cause almost no change in fuel use. This result indicates that making a rough pavement segment smoother through application of a maintenance or rehabilitation treatment will not result in substantially faster vehicle operating speeds, and therefore the benefits from reduced energy use and emissions due to reduced rolling resistance will not be offset by the increased fuel consumption that accompany increases in vehicle speed. However, efforts to develop a good model for predicting free-flow speed were not fully successful. The Southern California Interstate Freeway model developed yielded the best result with an adjusted Rsquared of 0.72. For the rest of the regions in the state, the selected explanatory variables can only explain about half of the total variance, meaning that there are still other variables, such as vehicle type, with a substantial impact on free-flow speed that were not covered in this study.

Comprehensive evaluation of automated pavement condition survey service providers' technical competence

High-quality pavement condition data are fundamental to pavement management, but the collection of these data is costly and time-consuming with steadily increasing complexity in equipment and procedures. Evaluating the technical competence of an automated pavement condition survey service provider presents unique challenges to highway agencies, because the technologies are rapidly advancing and the technical details are often outside the skill set of pavement engineers and managers. This study presents recent technological and procedural innovations in evaluating multiple bids for a statewide survey of California. These include evaluation on an item-by-item basis instead of comparing a derived composite index, combining several independent measurements of the same item to improve the quality of ground truth results and the synchronisation of profiles and pavement images to strengthen the credibility of assessment.

Initial Findings on Skid Resistance of Unpaved Roads

Unpaved roads have a dynamic surface, which can make it difficult to predict the skid resistance of a section for use in geometric design and gravel selection and to schedule maintenance. This investigation showed that there are three mechanisms for skidding on unpaved roads: intersurface friction, sliding on a thin layer of loose material, and plowing through a thick layer of loose material. The main surface and material properties affecting skid resistance are the stoniness severity and extent, the severity and extent of raveling, and the amount of loose material in the 0.850-mm to 2.00-mm range on the surface. The range of coefficients of friction for unpaved roads is from 0.40 to 0.85, with the lower value being conservative.

A spatial analysis of pavement variability

This paper looks at applying spatial statistics models to a number of types of data commonly available in pavement engineering, such as core thickness and air-void data, ground-penetrating radar-derived layer thicknesses and back-calculated stiffness values. The goals of the paper are to show how spatial statistics can be used to aid in the understanding of pavement-related data, to give some insights into what can and cannot be achieved, to aid pavement engineers exploring spatial statistics in understanding the results they are seeing in their analyses, and to help plan data collection and experiments. The focus of the paper is on determining the spatial properties, not in the use of the derived models in analysis, but some implications of spatial variability for pavement design are discussed.

Pavement structure segmentation method based on results derived from ground-penetrating radar data

This paper presents a pavement structure segmentation method based on the results derived from ground-penetrating radar (GPR) data. Surface type, layer thickness, base/sub-base type, shortest segment and outlier point criteria are developed to perform the segmentation. A boundary comparing method using t-tests is developed to perform layer thickness criteria. The value t = 4 is suggested for the HMA (hot mix asphalt) or aggregate layer; and t = 6 is suggested for Portland cement concrete and cement-treated base. During segmentation, segments that are shorter than the minimum length of 100 m are merged into adjacent segments. Furthermore, the flow charts of pavement structure segmentation as well as a procedure for automated segmentation are detailed. Proposed rules for fixing segment boundaries between GPR observations are also presented. Several segmentation examples are calculated by the automated procedure using the data from a pilot project in California. The results show that the method presented in this paper is reasonable for pavement structure segmentation based on GPR data.

Use of Ash in Low-Volume Road Construction in South Africa

Sasol Chemical Industries produces large quantities of coarse clinker and fly ash as a by-product of the coal gasification process at their Sasolburg plant in South Africa. If this ash could be used as an aggregate in roads, the demand on natural reserves for aggregates would be reduced and an effective method of disposing of these materials would result. The ash is processed at a blending plant in Sasolburg and is marketed under the name Premamix. Trial sections were constructed using labor-based techniques with unstabilized and bitumen emulsion-treated Premamix as a base course material. As the Premamix is a lightweight material and is delivered at a specified moisture content (the optimum moisture content for compaction), it is ideal for labor-based construction of low-volume roads as only spreading and compaction of the layers are required. The trial sections were subjected to accelerated pavement testing with the heavy-vehicle simulator. Although high deflections were measured in the pavement structure, the Premamix performed well under trafficking, even after the base was soaked with water.

Grouping Pavement Segments to Form Realistic Projects

The problem of selecting suitable segments for treatment in a pavement management system is complex because pavements exhibit considerable spatial variability in performance. This paper presents an algorithm for grouping small segments with uniform performance into large segments that are appropriate for a construction project. The algorithm works across lanes and along the traveled way and uses multiple years of predicted treatments rather than raw information on condition. A series of subprocedures is used to do the grouping, which is centered on a statistical approach to joining adjacent segments. A final treatment that is based on the treatments that were merged into the group is recommended, although the larger segments can also be used in the pavement management system, with aggregated performance, for optimization and project selection.

Calibration of ME Design Using a Combination of APT and PMS Data

The ultimate goal of ME design, and much of the pavement engineering behind it, such as APT, is to provide true estimates of how pavements will perform in the field. In the past, most design methods have been calibrated with APT data, and there has been some difficultly in calibrating to field data, including LTPP data. While this is partly due to a lack of understanding of the performance of pavements, much of the difficultly seems to arise because there is a disconnect between how performance is explained in design methods and how it is measured in the field. This paper details a framework for understanding APT data and PMS data, and for jointly modelling both sets while calibrating an ME design method.