Defect Detection in Reinforced Concrete Using Random Neural Architectures

This article discusses how detecting defects within reinforced concrete is vital to the safety and durability of infrastructure. A non-invasive technique, ElectroMagnetic Anomaly Detection (EMAD) is used in this article to provide information into the electromagnetic properties of reinforcing steel for which data analysis is currently performed visually. The first use of two neural network approaches to automate the analysis of this data is investigated in this article. These approaches are called Echo State Networks (ESNs) and Extreme Learning Machines (ELMs) where fast and efficient training procedures allow networks to be trained and evaluated in less time than traditional neural network approaches. Data collected from real-world concrete structures are analyzed in this article using these two approaches as well as using a simple threshold measure and a standard recurrent neural network. Two ESN architectures provided the best performance for a mesh-reinforced concrete structure, while the ELM approach offers a large improvement in the performance of a single tendon-reinforced structure.