Climate Representativeness of a Typical Meteorological Year for Long-Term Temperature Predictions in Rigid Pavements

Climatic conditions play a crucial role in determining the performance and service life of jointed plain concrete pavements (JPCPs). A significant climatic factor contributing to the development of stresses is temperature. Daily and seasonal temperature variations generate nonlinear temperature profiles in portland cement concrete (PCC) slabs, resulting in thermal stresses. The equivalent linear temperature difference (ΔT) through the slab leads to curling, which can cause cracks under vehicular loading. Additionally, the nonlinear temperature component of these distributions generates self-equilibrating eigenstresses, significantly contributing to total pavement stresses and fatigue damage. Traditionally, in mechanistic–empirical pavement design, hourly nonlinear temperature profiles are generated using historical weather data spanning several years, a time-intensive process. This study proposes an alternative approach using typical meteorological years (TMYs), which represent multiple years of weather data as a single year. TMYs were generated for two time periods, 1962–1991 and 1992–2021, for 24 Indian cities using the Sandia method. A one-dimensional finite-element model was used to compute PCC temperature profiles. The ΔT and eigenstresses evaluated using the TMYs and the full historic weather data were compared for both top-down and bottom-up cracking. Statistical comparisons indicate that TMYs represent mean climatic conditions well but do not account for extreme values. Furthermore, statistically significant differences in ΔT and eigenstresses between the two time periods highlight the importance of selecting an appropriate time frame for JPCP design to account for climate change.