"This interdisciplinary research proposes an advanced data analysis framework for civil infrastructure/structural health monitoring (I/SHM) based on a pioneering self-powered sensing technology. The current work characterizes the performance of a fairly new class of self-powered sensors for specific application problems with complex behavior. The proposed health monitoring systems are established through the integration of statistical, artificial intelligence and finite element methods. Different infrastructure systems with various damage types are analyzed. A new probabilistic artificial intelligence-based damage detection technique is developed that hybridizes genetic programming and logistic regression algorithms. The proposed multi-class classification system assigns probabilities to model scores to detect damage progression. A probabilistic neural network method based on Bayesian theory is further introduced to improve the damage detection accuracy. Data obtained from the finite element simulations and experimental study of hybrid sensor networks is used to calibrate the data interpretation algorithms. The network architecture comprises self-powered sensors that use the electrical energy directly harvested by piezoelectric ceramic Lead Zirconate Titanate (PZT) transducers. The beauty of this so-called self-powered monitoring system is that the operating power for the smart sensors directly comes from the signal being monitored. An advantage of using these sensors is that there is no need to directly measure the absolute value of strain in order to estimate damage. In fact, the proposed self-sustained sensing systems use the sensor output to relate the variation rate of strain distributions to the rate of damage. The proposed data analysis framework consists of multilevel strategies for structural/infrastructure damage identification through: (a) analysis of individual self-powered strain sensors, (b) data fusion in a network of self-powered strain sensors, and (c) data analysis in a hybrid network of self-powered accelerometer and strain sensors. For each of these levels, several damage indicator features are extracted upon the simulation of the compressed data stored in memory chips of the self-powered sensors. A new data fusion concept based on the effect of group of sensors, termed as "group effect", is proposed. The goal is to improve the damage detection performance through spatial measurements over structures. Moreover, combination of the data from a network of accelerometer and strain sensors results in developing an integrated global-local damage detection approach. The investigated cases are crack growth detection in steel plates under a uniaxial tension mode, distortion-induced fatigue cracking in steel bridge girders, continuous health monitoring of pavement systems, failure of simply supported beam under three-point bending, and failure of gusset plate of the I-35W highway bridge in Minneapolis, Minnesota. 3D dynamic finite element models are developed for each of the cases. The experimental studies are carried out on a steel plate subjected to an in-plane tension, a steel plate with bolted connections, and on asphalt concrete specimens in three-point bending mode. PZT-5A ceramic discs and PZT-5H bimorph accelerometers are placed on the surface of the plates to measure the delivered voltage in each damage phase. For the asphalt experiments, a new miniaturized spherical packaging system is designed and tested to protect the PZT ceramic discs embedded inside the specimen. Uncertainty analyses are performed through the contamination of the damage indicator features with different noise levels. The results indicate that the proposed I/SHM systems are efficiently capable of detecting different damage states in spite of high-level noise contamination."--Pages ii-iii.
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