, Professor, Civil, Environmental and Geo-Engineering
With our highway systems deteriorating, their timely monitoring and repair are essential. In performing such a task, a comprehensive and reliable assessment of the subgrade conditions, including the location of a seasonal or permanent stiff layer, provides an invaluable input for optimal design and scheduling of the rehabilitation strategies. The aim of this project is to develop an effective back-calculation method for interpreting the Falling Weight Deflectometer (FWD) data that is capable of reliably estimating the depth and stiffness properties of the seasonal stiff layers caused by frost or saturated soil. Prompted by the preliminary findings which indicate a marked correlation between the local resonance phenomena in the FWD response and the depth to groundwater table, the proposed method will employ a dynamic multi-layered model and utilize the velocity time histories in their entirety in order to delineate the location of the stiff layer. Performance of the method and the software developed will be validated with actual field data from the MnROAD site. Realization of the proposed work would encompass: (i) development of an appropriate neural network model for back-calculation of subgrade moduli and thicknesses; (ii) implementation of a computer program for estimating the effective moduli of an asphalt layer based on surface temperature measurements, (iii) generation of the synthetic data base containing dynamic velocity basins needed to train the neural network; (iv) validation of the neural network model versus field data from the MnROAD data base, and (v) recommendations for the effective shear and bulk moduli of frozen/saturated soils.