In their pavement management decision-making processes, U.S. state highway agencies are required to develop performance-based approaches by the Moving Ahead for Progress in the 21st Century (MAP-21) federal transportation legislation. One of the performance-based approaches to facilitate pavement management decision-making processes is the use of remaining service life (RSL) models. In this study, a detailed step-by-step methodology for the development of pavement performance and RSL prediction models for flexible and composite (asphalt concrete [AC] over jointed plain concrete pavement [JPCP]) pavement systems in Iowa is described. To develop such RSL models, pavement performance models based on statistics and artificial intelligence (AI) techniques were initially developed. While statistically defined pavement performance models were found to be accurate in predicting pavement performance at project level, AI-based pavement performance models were found to be successful in predicting pavement performance in network level analysis. Network level pavement performance models using both statistics and AI-based approaches were also developed to evaluate the relative success of these two models for network level pavement performance modeling. As part of this study, in the development of pavement RSL prediction models, automation tools for future pavement performance predictions were developed and used along with the threshold limits for various pavement performance indicators specified by the Federal Highway Administration. These RSL models will help engineers in decision-making processes at both network and project levels and for different types of pavement management business decisions.

The Federal Aviation Administration (FAA) has recognized for some time that its current rigid pavement design model, involving a single slab loaded at one edge by a single aircraft gear, is inadequate with respect to top-down cracking. Thus, one of the major observed failure modes for rigid pavements is poorly accounted for in the FAA Rigid and Flexible Iterative Elastic Layer Design (FAARFIELD) design software. A research version of the FAARFIELD design software has been developed in which the single-slab three-dimensional finite-element (3D-FE) response model is replaced by a four-slab 3D-FE model with initial temperature curling to produce reasonable thickness designs accounting for top-down cracking behavior. However, the long and unpredictable run times associated with the four-slab model and curled slabs make routine design with this model impractical. Artificial intelligence (AI)-based alternatives such as artificial neural networks (ANNs) have great potential to produce accurate stress predictions in a fraction of the time. ANNs could be practical replacements for a full 3D-FE computation that requires long computation times. In the development of ANN models, both individual input parameters and dimensional analysis have been considered, and accuracy of predictions from both methods was compared. ANN models for only mechanical and simultaneous mechanical and thermal loading cases were developed using individual input parameters and dimensional analysis. It was observed that very high accuracies were achieved in predicting pavement responses for all cases investigated.

Three-dimensional Finite Element (3D-FE) stress computations involved in the current rigid airport pavement design methodology, are time consuming when considering top-down cracking failure mode. In this study, Artificial Neural Network (ANN) models are integrated into a tool called ANNFAA to replace such 3D-FE computations. ANNFAA makes use of the best ANN models developed in MATLAB for 156 different airplanes without requiring any additional software installation or cumbersome learning of a new program. Within ANNFAA development, about 4,000 of 3D-FE simulations and many ANN models have been developed for each of these airplanes. Three useful tools were also developed using C# and MATLAB for implementing the 3D-FE analysis, post-processing the results, training the ANN models, and determining accuracy and performance of the ANN models. ANNFAA provides an accurate and rapid procedure for practitioners, engineers, and researchers for computing the critical stress responses associated with top-down cracking in multiple-slab rigid airfield pavements. This should make pavement design and analysis more practical, especially when a significantly large number of different cases that include top-down cracking failure mode are investigated. Also, this will help when currently used bottom-up cracking mode in the FAA standard rigid pavement design procedures is being considered in a design.

Widened slabs, widely employed in many US states in concrete pavements, have suffered from unexpected longitudinal cracks. These cracks suddenly appeared within 0.60 m to 1.20 m from widened slab edges and could be detrimental to the long-term pavement performance. The primary objective of this study was to identify possible causes for such longitudinal cracking observed on widened concrete pavements. Both field investigation and Finite Element Analysis were performed. Degrees of curling and warping were measured using a Terrestrial Laser Scanner. Concrete cores were also extracted to achieve a better understanding of how the cracking had developed. Field survey and numerical simulation results indicate that such longitudinal cracks could be primarily caused by a combination of excessive traffic loads, a high degree of curling and warping, inadequate support from underlying layers, pavement ageing, and skewed joints. It was also found that 4.30 m widened slabs coupled with tied Portland Cement Concrete shoulders outperformed others in terms of producing less cracking, even when they had experienced higher levels of truck traffic. The sites constructed in late afternoons also showed significantly less longitudinal cracks.