Mechanistic-empirical design procedures for continuously reinforced concrete pavement (CRCP) require characterization of variations in major design parameters so that a new or rehabilitated pavement can be designed for a desired level of reliability. Transverse cracking is an important CRCP design parameter affecting the prediction of crack width, crack load transfer efficiency, and critical stresses leading to longitudinal cracking and punchout development. The primary focus of this study was to investigate spatial characteristics of transverse cracking occurring in CRCP and to develop a theoretical model that would provide a means for systematic characterization of transverse crack spacing variability along the pavement length. Long-term pavement performance distress data were utilized to analyze transverse crack spacing characteristics for CRCP sections. From the results of the field data analysis, a theoretical model utilizing a Weibull distribution was developed to characterize the along-the-section transverse crack spacing frequency distribution. This theoretical model could be incorporated into the mechanistic-empirical procedures for CRCP structural design. The relationship between transverse crack spacing characteristics and punchout development and the location of the longitudinal cracks, which are a punchout precursor, were also analyzed using field survey data. Conclusions derived from field data analysis and the theoretical model presented here will be of interest to practicing engineers and researchers involved in CRCP design and performance modeling.

A rapid solution is presented for predicting critical tensile stresses on the top surface of continuously reinforced concrete pavements (CRCP). These tensile stresses are responsible for the development of CRCP punchouts and have to be considered in a mechanistic-based design procedure. The solution is based on a combination of the neural network (rapid solution) and finite element (numerical analysis) techniques. This approach combines the convenience and computational efficiency of neural network solutions with the flexibility and power of the finite element analysis. Such a combination is quite efficient for analyzing damage accumulation in CRCP, which requires predicting portland cement concrete tensile stresses for a large number of loading and site condition combinations. The procedure for stress prediction is based on the finite element model developed with ISLAB2000. The neural network has been trained with the results from ISLAB2000. A concept developed specifically for this study—the equivalent CRCP structure—was used extensively to reduce the number of independent parameters of the neural net work and speed up its training. The proposed rapid solution provides a good match of the ISLAB2000 stress values for a small fraction of the computation cost. This makes the rapid solution CRCP a natural choice for analyzing CRCP stresses and for inclusion in a mechanistic-empirical CRCP design procedure.

The concepts and approach used to develop a mechanistic-empirical structural design procedure for continuously reinforced concrete pavements (CRCP) are presented. Key aspects of the CRCP design procedure— including design overview, design inputs, structural response model, incremental damage analysis, and prediction of CRCP deterioration— are addressed. The mechanism of punch-out development is the foundation of the structural design procedure. Punch-out development is modeled by using mechanistic principles and damage accumulation over the design life. The incremental damage is used to account for changes in many factors throughout the entire design period, including material properties (portland cement concrete strength and modulus, erosion of base), seasonal climatic conditions, traffic loadings, crack load transfer, subgrade support, and others. Each analysis increment represents a specific combination of these factors during a selected period of time. Finally, accumulated damage is correlated with CRCP punchouts by using extensive field data, and a sensitivity analysis is provided to show that the procedure is reasonable.

The most frequent application of recycling materials in pavements is the reuse of reclaimed asphalt pavement (RAP) to produce recycled hot-mix asphalt (HMA). When designed properly, RAP mixes have demonstrated quality comparable to virgin HMAs in laboratory tests. Despite all the information available about the quality of RAP mixes, obstacles still promote their more frequent use in pavement engineering. Short- and long-term field performance of RAP mixes was investigated compared with virgin HMA overlays used in flexible pavements. Data from the 18 Specific Pavement Studies-5 (SPS-5) sites from the Long-Term Pavement Performance program located across the United States and Canada were used. Performance data were collected during periods ranging from 8 to 17 years. Repeated measures analysis of variance was the statistical analysis tool chosen, pairing distress measurements with survey dates to compare performance and response. The results suggest that in the majority of scenarios RAP mixes have performance statistically equivalent to virgin HMA mixes. The statistical equivalency of deflections suggests that RAP overlays can provide structural improvement equivalent to virgin HMA overlays.

Structural models in pavement management systems range from the simple to the relatively complex. The simplest models use deflections or deflection basin parameters to characterize subgrade and pavement structural properties, while the more complex ones use pavement layer moduli (derived from deflections) and pavement layer thicknesses and material types to calculate pavement response, which is then used to predict failure, much like project-level pavement design analysis. Any pavement management system using the latter, more complex approach would undoubtedly need more defection information. In any case, deflection data collection is expensive and time-consuming. Most states and local agencies have few falling weight deflectometers, which are used mainly to collect project-level deflection data for scoping maintenance and rehabilitation work at the project level and for research purposes. This study explores the use of models to estimate the error associated with the choice of different deflection test spacings that can be used to optimize data collection for more efficient and economical applications in pavement management systems.