Bridge deck construction quality assurance/quality control and performance prediction incorporating point cloud data analysis

In bridge assessment practices, the field remains strongly tied to traditional methods, with owner agencies relying heavily on accumulated heuristic knowledge to interpret data and make decisions based on standard inspections and load ratings. As a result, rapidly deployable technologies such as wireless, non-contact, or remote sensing are important for the future of structural health monitoring. Towards that end, the aim of this research was to explore the use of remote sensing, such as terrestrial laser scanners, to support and improve the assessment of bridge deck condition. Two terrestrial laser scanner (TLS) application scenarios are presented for the characterisation of longitudinal and transverse profiles that affect the performance of the deck against appropriate water runoff, hindering the condition of the deck due to the development of corrosion. These profiles are compared to electrical resistivity and rebar cover available from the ground penetrating radar data collected. The profile of the evaluated bridge decks was captured within the accuracy of the TLS (±1 mm over 10 m). TLS-derived point cloud data enable accurate geometric modelling of bridge decks, supporting condition assessment by identifying regions with reduced rebar cover and surface slopes associated with increased deterioration risk.