Low reliability and high maintenance cost of using power and data cables are two main reasons motivating the application of the self-powered wireless sensors for structural health monitoring (SHM) systems in bridge structures. On the other hand, energy harvesting systems have been introduced as a solution for the current limitations of the batterypowered wireless sensors associated with the finite life-span of batteries and their replacements. The objective in this paper is to propose a new optimized nonlinear energy harvesting concept, namely Bistable Energy Harvesting (BEH) system, for smart SHM of bridge structures. In this study, a dynamic analysis of the energy harvesting system for cablesupported bridges subject to wind-induced vibration is carried out and the feasibility of the energy harvesting device is investigated. This paper presents efficient linear and nonlinear energy harvesting systems for wireless monitoring of long-span cable-supported bridges. It is shown that level of the extracted energy from such energy harvesting system is quite sufficient to supply energy for self-powered sensors of a bridge health monitoring system. This study is to promote the recent line of research on self-powered sensor networks for smart bridge monitoring being performed at the Florida International University.
The Surface Response to Excitation (SuRE) method is a guided-wave based Structural Health Monitoring (SHM) technique. Up to date, no analytical model has been developed and validated for the SuRE method. This paper experimentally and analytically investigates the delamination between two plates using the SuRE method in conjunction with the COMSOL Multiphysics software. Simulation results are validated by experimental results. The results showed that the findings from the analytical approach correspond with the experimental results and can be effectively used for studying delamination. This approach can be utilized for different types of structures with similar conditions.
Composite is one of the most widely used industrial materials because of high strength, low weight, and high corrosion resistance properties. Different parts of composite structures are normally joined using adhesives or fasteners that are prone to defects and damages. A reliable method for prediction of the defect location is needed for an efficient structural health monitoring (SHM) process. Heterodyne effect is recently utilized for damage detection in the bonding zone of composite structures where debonding is expected to change the linear characteristics of the system into nonlinear characteristics. This paper briefly introduces this novel defect locating approach in composite plates using the heterodyne effect. For the first time, an Artificial Neural Network methodology is utilized with heterodyne effect method to find the defect location in composite plates. The main objective of this article is to develop a neural network based methodology for prediction of damage location, particularly for the bond inspection of composite plates.