Pavements--Skid resistance

This project illustrates the neural network approach to constructing a fuzzy logic decision system. This technique employs an artificial neural network (ANN) to recognize the relationships that exit between the various inputs and outputs. An ANN is constructed based on the variables present in the application. The network is trained and tested. Various training methods are explored, some of which include auxiliary input and output columns. After successful testing, the ANN is exposed to new data and the results are grouped into fuzzy membership sets based membership evaluation rules. This data grouping forms the basis of a new ANN. The network is now trained and tested with the fuzzy membership data. New data is presented to the trained network and the results form the fuzzy implications. This approach is used to compute skid resistance values from G-analyst accelerometer readings on open grid bridge decks.

An analytical method is proposed for the response analysis of lifeline structures subjected to earthquake excitations. The main feature of the approach is to consider the vibrational motion as a result of the wave motion in a waveguide-like lifeline structure. Based on the theory of wave propagation, scattering matrices are derived to characterize the wave propagation in individual segments and wave reflections and transmissions at supports and boundaries. Response solution is derived in a closed form, suitable for stochastic analysis when the input is an earthquake excitation. A space-time earthquake ground motion model that accounts for both coherent decay and seismic wave propagation is used to specify motions at supports. The proposed technique can be used to obtain lifeline structural response accurately and determine the correlation between any two locations in an effective manner. The computational aspects of its implementation are also discussed. Numerical examples are presented to illustrate the application and efficiency of the proposed analytical scheme.