Sensor network application framework for autonomous structural health monitoring of bridges

Life cycle monitoring of civil infrastructure such as bridges and buildings is critical to the long-term operational cost and safety of aging structures. Nevertheless, there is no commonly accepted and recognized way to perform automated monitoring of bridges. One of the important issues is the cost of the data acquisition subsystem and its installation and maintenance costs, which are tightly connected to the choice of monitoring methodology. The presented application framework includes: first, Wireless Intelligent Sensor and Actuator Network (WISAN) as an inexpensive way to perform data acquisition for the tasks of structural health monitoring; second, a vibration-based SHM method for bridges; and third, a fully autonomous SHM system for bridges, ambient-energy-powered and minimally dependent of human involvement. Design of the sensor network reflects the particularities of the application: proactive rather than reactive nature of the data streams; fault-tolerant architecture ensuring protection from extreme events; and real-time data acquisition capabilities. Other issues include operating a massive array of heterogeneous sensors, achieving a low cost per sensor, cost and sources of energy for the network nodes, energy-efficient distribution of the computational load, security of communications and coexistence in the ISM radio bands. The modal SHM methods under consideration are the method of modal strain energy with fuzzy uncertainty management, method of damage index and a method based on Hilbert-Huang transform. Modal identification through ambient vibrations is performed though auto-regressive moving average models. The final step in the monitoring methods is the determination of bridge deterioration rate and prediction of its remaining useful life based on measurements provided by the sensor network and modal methods used. The deterioration curves are generated at both the element and bridge levels and are compared to existing inspection-based methods.