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Concrete pavements develop cracks with the accumulation of fatigue damage caused by the combined effect of vehicular and environmental loadings. In Mechanistic-Empirical (ME) design, the ratio of stresses to some measure of strength is used to evaluate incremental damage, which is then related to cracking. Typically, this measure is the Modulus of Rupture (MOR) of a beam made of the same material. The flexural strength is also known to vary with the thickness of the beam. However, the variation of flexural strength due the presence of eigenstresses from temperature or moisture distributions has not received sufficient attention. A non-linear fracture mechanics model demonstrated that the thickness-adjusted flexural strength can also be used for characterising the maximum elastic stress under a linear temperature distribution. However, under a non-linear temperature distribution, the material may sustain a higher or lower maximum elastic stress than the flexural strength. To model this, a practical analytical expression for the apparent flexural strength was derived and showed excellent agreement with numerical simulations as well as with a model based on equivalent elastic deformation energy. The model was used to show that the fatigue capacity under a typical axle load varies with various non-linear temperature distributions. [ABSTRACT FROM AUTHOR]

Superload (SL) vehicles have unique axle configurations and high axle weights, indicating the potential for a low number of SL applications to cause significant fatigue damage to a jointed plain concrete pavement (JPCP). The current JPCP fatigue model in the AASHTO Pavement Mechanistic-Empirical (ME) Design Guide is unable to account for damage caused by SLs because the stress prediction models within it do not consider these unique axle configurations. As a result, it is not possible to account for the potential fatigue damage accumulation caused by SL applications or identify critical conditions that contribute to damage accumulation using Pavement ME. To address this, a critical, SL stress-prediction model was developed that can be used along with the current fatigue damage model. Typical SL configurations in Pennsylvania were identified based on available permit data, and a database of critical tensile stresses generated by these SLs for various JPCP structures was developed using finite element analysis. This database was used to train a series of artificial neural networks (ANNs), which predict critical tensile stress as a function of SL axle configuration, temperature gradient, and pavement structure. The method for determining fatigue damage accumulation using the ANNs to predict stresses is then presented. [ABSTRACT FROM AUTHOR]

Temperature gradients in concrete pavements significantly affect fatigue damage accumulation, with high gradients having the potential to reduce performance under vehicle loads. While temperature profiles are non-linear throughout the depth, an equivalent linear temperature gradient (ELTG) accounts for the effect of temperature in pavement design. Critical ELTG values are difficult to predict because ELTG is affected by design parameters as well as climate conditions and varies diurnally and seasonally. To identify critical parameters which affect ELTG, a database of hourly temperature profiles was generated for a series of jointed plain concrete pavements (JPCP) throughout Pennsylvania. A stepwise regression model was developed with an R2 of 0.81 to determine maximum daily ELTG as a function of the design parameters and climate. This equation can assist engineers in making decisions on when superloads can be transported without significant damage or performing falling weight deflectometer testing will be most informative. Slab thickness, concrete albedo, and daily temperature range (DTR) were the most significant parameters that affected ELTG. A higher albedo showed an equivalent effect in reducing maximum ELTG as increasing the thickness of the slab. This finding can be used to reduce maximum gradients, which will increase the predicted service life of JPCPs. [ABSTRACT FROM AUTHOR]

Transverse joint faulting is a distress observed in bonded concrete overlays of asphalt pavements (BCOAs). However, to date, there is no predictive faulting model for BCOAs. Therefore, the objective of this research is to develop such a model. First, models were developed to predict the structural response of BCOAs due to environmental and traffic loads. Previously-developed artificial neural networks that rapidly estimate the structural response of BCOAs at the joint due to these loads was used to relate the structural response to the damage using the differential energy (DE) concept. Next, DE was related to faulting through an incremental analysis considering traffic, climate, and joint deterioration. Finally, a calibration using performance data from existing BCOAs throughout the continental United States and an extensive sensitivity analysis on the model's prediction capabilities was performed. This faulting prediction model has been incorporated into the BCOA-ME design guide developed by the University of Pittsburgh. [ABSTRACT FROM AUTHOR]

Albedo in the context of the Urban Heat Island contributes to the environmental impact of a pavement. Measuring the pavement albedo is difficult as it depends on the size of the specimen, background interference, and variations in the incoming solar spectrum. For this study, the albedo was determined for a 1-m2 concrete slab cast with white cement containing titanium dioxide nanoparticles. The size of the exposed surface of the slab was varied using black paper. To calculate the albedo of these nonstandard specimen sizes, a new testing technique was proposed that uses an albedometer and various geometric conditions, including the exposed slab surface and measurement height. The albedo of the finite-sized concrete specimen was found to range from 0.50 to 0.55, depending on the number of unknowns assumed, with the most reasonable estimate of 0.54. The measured slab albedo from the new method was also verified using a laboratory spectrophotometer. With this proposed methodology to account for background surfaces, the albedo of laboratory-sized concrete specimens of any shape can now be easily measured at various test site locations. [ABSTRACT FROM AUTHOR]

Pavement preservation (PP) is a planned set of construction and material interventions that can extend the pavement’s service life and may also impact sustainability through Heat Island (HI) mitigation. The HI mitigation potential can vary from location-to-location and with time. For agencies to widely adopt the PP, it is necessary to quantify the benefits based on the context of the project. A method to calculate the Global Warming Potential (GWP) for the HI effect was developed and illustrated for four cities in the US: Chicago, Austin, San Diego and Philadelphia, for hypothetical pavements with three preservation options: chip seals, a concrete inlay, and an asphalt concrete inlay. The use phase GWP with respect to HI was estimated for all cases given a 2-, 5-, 7- or 10-year service life. Overall, the HI in the use phase was found to dominate the total GWP relative to the materials and construction phases. The HI GWP savings increase over time, with the 10-year savings being greatest for San Diego using the concrete inlay (22.5 kg CO2-eq/m2) and smallest for Chicago with a chip seal (8.0 kg CO2-eq/m2). The savings were found to increase in areas that have a more pronounced HI and could offset GWP in the other phases. The proposed method allows agencies to estimate HI GWP for a specific preservation strategy, location and service life. [ABSTRACT FROM AUTHOR]