Automatic Calibration of Tunnel Volume Loss Using Leapfrog and the Limaniv Method

In increasingly congested cities, tunnels are being used for new transportation routes. It is important to be able to predict the ground movement resulting from tunnel excavation, in order to manage its impact on existing infrastructure. This paper describes a procedure for calibrating settlement trough parameters automatically, using the Limaniv method, in combination with Leapfrog models and Python scripting. This method predicts surface settlement, due to elastic squeezing of an excavated tunnel, due to the weight above that tunnel. This process allows parameters to vary continuously along the tunnel, providing a more accurate settlement prediction, and removing the need to apply overall highly conservative values. A weighting function, derived and presented in this paper, is used to obtain a single equivalent Young’s modulus value from multi-layered ground conditions. The code is implemented as a plugin to the Geographical Information Systems program, QGIS, and an example is given with a Leapfrog model of a location in Sydney, Australia. Settlement predictions from this approach are validated against numerical modeling.