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The study of dynamic response of Timoshenko beam traversed by moving load subjected to random base excitation is carried out. By applying the theory of dynamic response of Timoshenko beam as well as finite element theory, beam finite element governing equations of motion are developed and they are solved using Galerkin method. To validate the model, some results of the model are compared with those available in literatures and very close agreement is achieved. The beam is subjected to travelling load and random base excitation in lateral direction simultaneously. Three types of boundary conditions, namely, hinged-hinged, hinged-clamped, and the clamped-clamped ends, are considered and beam dynamic behavior; such as deflection, velocity, and bending moment of beam midpoint, with all so-called boundary conditions are studied. To get better understanding of base excitation effects on the beam dynamic performance, all the results are presented with and without base excitation, in which considerably difference is observed. Moreover, the effect of base excitation on beam with different span-length is monitored.

A new approach for on-track measurement of the lateral/vertical contact forces is presented in this paper. The proposed method is based on measurement of the strain at two sides of the wheel web. Electric signals generated by the strain gauges are fed into a neural network algorithm in order to predict the lateral/vertical contact forces. Feed-forward technique is used in the neural network algorithm. A sensitivity analysis has been carried out to find the best position for the strain gauges. A dynamic model of a freight wagon is provided and a variety of numerical simulations are performed to obtain the probability distribution of the lateral and vertical contact forces. The obtained probability distribution function is then utilized to generate lateral/vertical contact forces within the practical range. In order to train the neural network system, the generated contact forces are applied to the wheel flange and the strain signals are obtained. More than 100 configurations are fed into the system in order to train it. Reliability, accuracy and sensitivity of the proposed measurement system are then investigated.