Experimental Data

Finite element models of current structures often behave differently than the structure itself. Model updating techniques are used to enhance the capabilities of the numerical model such that it behaves like the real structure. Experimental data is used in model updating techniques to identify the parameters of the numerical model. In civil infrastructure these model updating techniques use either static or dynamic measurements, separately. This paper studies how a Bayesian updating framework behaves when both static and dynamic data are used to updated the model. Displacements at specific structure locations are obtained for static tests using a computer vision method. High density mode shapes and natural frequencies are obtained using a moving accelerometer structure. The static data and the modal characteristics are combined in a Bayesian modal updating technique that accounts for the incompleteness and uncertainty of the data as well as the possible nonuniqueness of the solution. Results show how the posterior probability density function changes when different type of information is included for updating.

The main goal of this specific study is to reveal the benefits from the use of the ANN in geotechnical engineering by focusing on the elaboration of data coming from real field measurements concerning ground movements due to tunnelling. Moreover, the Back Propagation algorithm is used so as to reveal the hierarchy of each of the parameters mainly affecting the total amount of the induced settlements due to tunnelling. The short review of our experience in applying ANN's in Geotechnical Engineering is succeeded by the main accomplishments of this study. The provided information, originally published by Attewell, concerns tunnel size and depth, maximum settlement, settlement trough width, volume and slope, ground description, geological properties and excavation method.