Recent advances in sensor technology promote using large sensor networks to efficiently and economically monitor, identify and quantify damage in structures. In structural health monitoring (SHM) systems, the effectiveness and reliability of the sensor network are crucial to determine the optimal number and locations of sensors in SHM systems. Here, we suggest a probabilistic approach for identifying the optimal number and locations of sensors for SHM. We demonstrate a methodology to establish the probability distribution function that identifies the optimal sensor locations such that damage detection is enhanced. The approach is based on using the weights of a neural network trained from simulations using a priori knowledge about damage locations and damage severities to generate a normalized probability distribution function for optimal sensor allocation. We also demonstrate that the optimal sensor network can be related to the highest probability of detection (POD). The redundancy of the proposed sensor network is examined using a 'leave one sensor out' analysis. A prestressed concrete bridge is selected as a case study to demonstrate the effectiveness of the proposed method. The results show that the proposed approach can provide a robust design for sensor networks that are more efficient than a uniform distribution of sensors on a structure.
In this paper, an innovative field application of a structural health monitoring (SHM) system using field programmable gate array (FPGA) technology and wireless communication is presented. The new SHM system was installed to monitor a reinforced concrete (RC) bridge on Interstate 40 (I-40) in Tucumcari, New Mexico. This newly installed system allows continuous remote monitoring of this bridge using solar power. Details of the SHM component design and installation are discussed. The integration of FPGA and solar power technologies make it possible to remotely monitor infrastructure with limited access to power. Furthermore, the use of FPGA technology enables smart monitoring where data communication takes place on-need (when damage warning signs are met) and on-demand for periodic monitoring of the bridge. Such a system enables a significant cut in communication cost and power demands which are two challenges during SHM operation. Finally, a three-dimensional finite element (FE) model of the bridge was developed and calibrated using a static loading field test. This model is then used for simulating damage occurrence on the bridge. Using the proposed automation process for SHM will reduce human intervention significantly and can save millions of dollars currently spent on prescheduled inspection of critical infrastructure worldwide.
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