A modified method for improving the prediction accuracy of the tunnel shaking table model test based on non-direct similarity technique

Abstract

The tunnel shaking table model test has many influencing factors, and the test parameters are difficult to meet the strict similarity ratio. There are often large errors in predicting prototypes directly using the similarity ratio derived from the classical similarity theory. In order to improve the prediction accuracy of the tunnel shaking table model test, this article proposes a modified method of the traditional similarity theory. Based on the traditional dimensional analysis method, this method uses a non-direct similarity technique to rebuild the dimensional matrix for the main test parameters, derive a new similarity criterion, and then obtain a new similarity ratio. Different from the traditional similarity ratio which is a certain value, the new similarity ratio varies with dynamic parameters, which is more consistent with the actual situation. The tunnel shaking table model test and numerical simulation are carried out to verify the method. Experiments show that the modified method is superior to the traditional similarity theory in numerical prediction accuracy.