抄録
This paper proposes a method of inverse analysis for identification of an impact load using selected strain data. Taking the dynamic behavior of structure under the impact load is comparatively easy if many sensors and high precision measuring devices are available. On the other hand, if the dynamic behavior can be obtained by a few strain data with lower accuracy, the method has advantages to measuring cost and efficiency. In this study, Akaike's Information Criterion is used for determining optimum number of the hidden neuron for the neural network. As a result, it is shown that the proposed method using Akaike's Information Criterion has an advantage on the inverse analysis.
