Soft computing based parameter identification in pavements and geomechanical systems

摘要

Accurate estimation of road pavement geometry and layer material properties throughthe use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by thenondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem.In this dissertation, a hybrid algorithm was developed to seek robust and fast solutionsto this inverse problem. The algorithm is based on soft computing techniques, mainlyArtificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analysesof flexible pavements of various pavement types; including full-depth asphalt andconventional flexible pavements, were built on either lime stabilized soils or untreatedsubgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft ComputingBased System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models.The solution to the inverse problem for multi-layered pavements is computationally hardto achieve and is often not feasible due to field variability and quality of the collecteddata. The primary difficulty in the analysis arises from the substantial increase in thedegree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS modelperformances. Still, SOFTSYS models were shown to work effectively with the syntheticdata obtained from ILLI-PAVE finite element solutions.In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data weresuccessfully used to predict pavement layer thicknesses and layer moduli of in-serviceflexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thicknessestimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively.The field validations of SOFTSYS data also produced meaningful results. The thicknessdata obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils comparedto conventional flexible pavements. Overall, SOFTSYS was capable of producing reliablethickness estimates despite the variability of field constructed asphalt layer thicknesses.