Emerging city-scale damage prediction options for urban tunnelling

Predicted world population increases coupled with urbanization trends will result in ever heightened use of subsurface spaces, which means deeper and larger excavations and more extensive tunnelling. Predicting and, ultimately, preventing affiliated subsidence- and vibration-induced damage will be a major challenge for the engineering community over the next century, especially with respect to historic, unreinforced masonry buildings and bridges due to their low tensile capacity and, thus, their inherent inability to accommodate displacements and distortions without damage. This paper will present how remote sensing technologies may usher in a new generation of city-scale methods for tunnel damage prediction. Critical to this is the ability to auto-populate a city-scale computational model. This paper demonstrates how aerial laser scanning, hyperspectral imagery, thermal imagery, and unmanned aerial vehicles may represent key enabling technologies to achieving this goal.