Advance Reconnaissance and Optimal Monitoring

Abstract

Effective exploration techniques during mechanized tunneling are of high importance in order to prevent severe surface settlements as well as a damage of the tunnel boring machine, which in turn would lead to additional costs and a standstill in the construction process. A seismic methodology called full waveform inversion can bring a considerable improvement compared to state-of-the-art seismic methods in terms of precision. Another method of exploration during mechanized tunneling is to continuously monitor subsurface behavior and then use this data to identify disturbances through pattern recognition and machine learning techniques. Various probabilistic methods for conducting system identification and proposing an appropriate monitoring plan are developed in this regard. Furthermore, ground conditions can be determined by studying boring machine data collected during the excavation. The active and passive obtained data during performance of a shield driven machine were used to estimate soil parameters. The monitoring campaign can be extended to include above-ground structural surveillance as well as terrestrial and satellite data to track displacements of existing infrastructure caused by tunneling. The available radar data for the Wehrhahn-line project are displayed and were utilized to precisely monitor the process of anticipated uplift by injections and any subsequent ground building settlements.