In situ crack mapping of large-scale self-sensing concrete pavements using electrical resistance tomography

Abstract

This study aims to validate the large-scale application of self-sensing concrete in airport runway pavements and to use electrical resistance tomography (ERT) for characterizing spatially distributed damage during accelerated pavement testing. This self-sensing concrete not only retains the expected mechanical properties of typical airport pavements, but it can also sense deformation and strain. First, sensing properties were encoded in concrete pavements by modifying the cement-aggregate interface with multi-walled carbon nanotube (MWCNT) thin films. MWCNT thin films were spray-coated onto dried fine and coarse aggregates, and the film-coated aggregates were directly used for concrete casting. Second, an ERT algorithm was implemented for spatial conductivity mapping of self-sensing concrete pavements. Extensive laboratory tests were conducted on different sized specimens for characterizing their spatial damage detection performance. Last, a full-scale concrete airport pavement slab was cast with self-sensing concrete patches at locations where damage was expected. A heavy vehicle simulator was employed for accelerated pavement testing to induce cracks, while ERT measurements were collected at periodic intervals during testing. The results confirmed that the severities, locations, and patterns of cracks could be identified from the reconstructed ERT conductivity maps. Furthermore, subsurface damage features were identified prior to these cracks propagated and became visible on the surface.