Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks (2022)
Journal Article
Wootton, A. J., Day, C., & Haycock, P. W. (2022). Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks. Structural Health Monitoring, 21(6), 2910-2921. https://doi.org/10.1177/14759217221080718

The degradation of roads is an expensive problem: in the UK alone, £27 billion was spent on road repairs between 2013 and 2019. One potential cost-saver is the early, non-destructive detection of faults. There are many available techniques, each with... Read More about Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks.

Defect detection in reinforced concrete using random neural architectures (2013)
Journal Article
Butcher, J., Day, C., Austin, J., Haycock, P., Verstraeten, D., & Schrauwen, B. (2014). Defect detection in reinforced concrete using random neural architectures. Computer-Aided Civil and Infrastructure Engineering, 29(3), 191-207. https://doi.org/10.1111/mice.12039

Detecting defects within reinforced concrete is vital to the safety and durability of our built infrastructure upon which we heavily rely. In this work a non-invasive technique, ElectroMagnetic Anomaly Detection (EMAD), is used which provides informa... Read More about Defect detection in reinforced concrete using random neural architectures.