PREDICTION OF LAYER MODULI FROM FALLING WEIGHT DEFLECTOMETER AND SURFACE WAVE MEASUREMENTS USING ARTIFICIAL NEURAL NETWORK
ridership - elasticity, ridership - commuting, ridership - forecasting, ridership - forecasting
Thickness, Surface waves, Scenarios, Rayleigh waves, Projections, Phase velocities, Pavement layers, Neural networks, Modulus of subgrade reaction, Modulus of elasticity, FWDs, Forecasting, Falling weight deflectometers, Elasticity modulus, Depth, Coefficient of subgrade reaction, Bedrock, Backcalculation, Artificial neural networks, ANNs (Artificial neural networks), Algorithms
A new algorithm for predicting layer moduli using measurements from both falling weight deflectometer (FWD) and surface wave tests is presented. This algorithm employs numerical solutions of a multilayered half-space based on Hankel transforms as a forward model and an artificial neural network (ANN) for the inversion process. Phase velocities for frequencies ranging from 10 Hz to 10,000 Hz are calculated using the forward model for varying pavement structures with a range of layer moduli and thicknesses. These phase velocities, along with the layer moduli and thicknesses, are used to train an ANN to backcalculate layer moduli from dispersion curves (i.e., phase velocity versus frequency curves) constructed from the FWD and stress wave test data. To account for the effect of bedrock on the moduli prediction, another network is trained with layer thicknesses and phase velocities for predicting the depth to bedrock. Combining this network with the network for the moduli prediction results in a sequential dispersion analysis technique in which the depth to bedrock predicted from the first network becomes an input to the second network for predicting layer moduli. FWD and stress wave test measurements from an intact pavement and an asphalt overlay over cracked asphalt layer are processed using the sequential dispersion analysis technique and MODULUS 5.0 backcalculation program. Comparison of the results indicates that the dispersion analysis technique yields less variable subgrade moduli and is more sensitive to changes in the asphalt surface layer, because the high-frequency data from the stress wave test is incorporated.
Kim, Y, Kim, Y. (1998). PREDICTION OF LAYER MODULI FROM FALLING WEIGHT DEFLECTOMETER AND SURFACE WAVE MEASUREMENTS USING ARTIFICIAL NEURAL NETWORK. Transportation Research Record, Vol. 1639, p. 53-61.