Bridges

Traditional inspection procedure for condition assessment of bridge structures is laborious, dangerous, time-consuming, capital intensive and highly dependent on subjective human judgment. Subsequently, bridge asset owners pay a high price per inspection and are sometimes unable to inspect their structures more frequently. The authors present an improved defect detection and quantification algorithm paired with a novel Unmanned Aerial Vehicle (UAV)-based data collection technology to detect and quantify surface and subsurface defects such as delamination, voids, and cracking. The data is collected in terms of Light Detection and Ranging (LiDAR), optical images, infrared images, and acoustic signatures and combined to quantify surface and subsurface defects in concrete bridges. Furthermore, the condition assessment on a time scale allows owners to make cost-efficient business decisions on what to repair and when to repair using the risk modelling methods. Hence, it extends the structure's lifespan rather than replacing the entire structure. This approach also allows for building predictive deterioration models using historical performance, which can be utilised for asset management. This methodology was applied at a small scale on multiple bridge structures located in Canada, USA, and Australia. The multi-layered data helped create a baseline model of the structures that can be tracked over a period. The concrete deterioration, such as cracking, spalling, and delamination, were detected, quantified, and mapped to feed into the risk model. With this model, the asset owner can objectively determine whether further checking of a specific element, material research or a complete detailed re-examination is necessary, thereby leading to considerable savings in time and social costs. This method applied with several years of data will provide the predictive capability to determine the future behaviour of the structure as well as provide the urgency with where and what reparation or further research is required across the asset portfolio. Data gathering over the years will transform how we manage our structures and inform the optimisation of the durability and resilience of future structures.