Non-destructive evaluation (NDE) has generated great interest in assessing structural capacity of road, highway and airfield pavements. While considerable effort has been devoted to static analysis based inversion, little has been done to investigate inverse problems based on dynamic analysis while taking uncertainty into account. The proposed research will establish a rigorous and unified theoretical framework for pavement NDE that integrates statistical science, optimization theory, and computer simulation, and will develop an elastodynamic inverse analysis software toolkit for practical engineering applications. By using the entire time history of forward dynamic analysis in inversion, the proposed research will provide more stable and more accurate results. By formulating unknown parameter reconstruction using maximum likelihood estimate and its robust counterpart within the framework of Bayesian decision theory and Markov Chain Monte Carlo simulation, the research promises to bring tremendous benefits. These include allowing uncertainty to be treated naturally, allowing the effect of measurement noise and outliers to be mitigated, allowing engineers' experience to be incorporated as a priori information, and enabling the accuracy and reliability of reconstructed parameters to be quantitatively assessed using the provided parameter uncertainty. By investigating a variety of classical and non-classical optimizations algorithm and artificial neural network in the context of NDE in both the time and the transformed domain, the research promises to develop efficient, accurate and robust optimization methods for the implementation of the project, and to retrieve much richer information of unknown physical properties from measured pavement responses. The proposed methodological framework can be generally applicable to similar types of NDE, thereby adding new scientific knowledge to other fields. The project will generate cost-effective, fast and reliable pavement NDE techniques, which will result in significant economic benefits. As an inter-disciplinary research effort, the project will provide students cross-disciplinary exposure and unique experience in analytical, computational and experimental work.
