The analysis of concrete pavement profiles has been an important part of pavement management for years. “Built-in” curling is a key input parameter for the Mechanistic-Empirical Pavement Design Guide (MEPDG). Built-in curling pertains to permanent curvatures found in concrete slabs due to early age properties. Currently, no comprehensive procedure exists to model or estimate the long-term, effective built-in curling.
Curling refers to the way a concrete slab changes curvature due to an internal temperature gradient. Concrete surface profiles possess and show curl within slabs. However, due to the magnitudes of deflection from temperature gradients, extracting these temperature induced curl profiles can be difficult. For example, for a 12’x15’, 10” thick, single concrete slab containing a 15°F temperature gradient, computational models predict a maximum deflection of approximately 0.0174 inches, transversely from the middle of a slab to the edge. This small magnitude of deflection, coupled with the error involved in recording road profile data out in the field, makes it difficult to find consistencies in data that is not generated artificially.
A Hilbert-Huang Transform (HHT) based algorithm was developed to analyze both field road profile data and artificially generated slab profile data in the hope that smoothed, consistent profiles could be extracted from noise-filled data sets using empirical mode decomposition (EMD). The application of this algorithm to concrete surface profiles resulted in the successful separation of the intrinsic mode functions contained within the data. The separation revealed intrinsic mode functions correlating to “noise”, “curl" and “base trend” data. ISLAB2005 artificial slab profiles, containing randomly induced error, were clearly identified. Trends in ALPS2, Minnesota IRI, Wisconsin LTPP, Georgia LTPP, Utah LTPP, and Arizona LTPP sections were also found. Arizona LTPP slab profiles were shown to contain consistent “curl” deflections for the same slab over a 20 month time-span and during both winter and early fall seasons. The consistent slab shape is likely due to early age built-in curl. Some of the profiles analyzed appeared to be dominated by noise.
Artificial pavement profiles, corresponding to wide ranges of temperature gradients (-30°F to +30°F or ≈ -34.4°C to -1.1°C), were generated using the finite element program ISLAB2005. Cubic splines were applied through FORTRAN software to build a computationally efficient slab surface model, capable of back-calculating temperature gradients through artificially generated slab profiles. Optimization packages in DAKOTA used this FORTRAN model to back-calculate temperature gradients for ISLAB2005 slab profiles with known thicknesses, lengths and surface profiles. ISLAB2005 slab profiles containing induced random error were successfully smoothed by applying the developed Hilbert-Huang based profile analysis algorithm, and their corresponding temperature gradients were accurately back-calculated. Real road slab profiles were also smoothed using the Hilbert-Huang based algorithm, but the magnitudes of their deflections correlated to extreme temperature gradients in ISLAB2005. The large deflections are likely due to built-in curl, and the correlating extreme temperature gradient from ISLAB2005 is due to the model not taking into account early age built-in curl during temperature deflection estimations.