Neural network for structural health monitoring with combined direct and indirect methods

Advancement in wireless communication as well as recording and transferring data over the internet provides a lot of possibilities for smart inspection and monitoring for machines and structures. The big data recorded and transferred through such a system must be analyzed efficiently on the go to provide accurate feedback to the system. Neural network (NN) data processing techniques are an effective methodology for fast and accurate analyses of the data and provide feedback to the system. An NN methodology is proposed for structural health monitoring of bridge structures. The proposed platform uses the direct and indirect sensors mounted on the bridge structure and on the passing vehicle, respectively. This proposed approach will decrease the cost and the potential damages to the sensors in direct methods, and will increase the accuracy and reliability of monitoring in indirect techniques. The methodology and data processing techniques have been validated using a lab-scaled test bed.