Defect detection in reinforced concrete using random neural architectures

Abstract

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 information into the electromagnetic properties of the reinforcing steel and for which data analysis is currently performed visually: an undesirable process. This article investigates the first use of two neural network approaches to automate the analysis of this data: 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 have been analyzed using these two approaches as well as using a simple threshold measure and a standard recurrent neural network. The ELM approach offers a significant improvement in performance for a single tendon-reinforced structure, while two ESN architectures provided best performance for a mesh-reinforced concrete structure.