"This research proposes novel damage progression quantification and data robustness evaluation approaches, for structural health monitoring (SHM), using a new class of self-powered piezo-floating-gate (PFG) sensors. This system relies on harvesting the mechanical energy from structures through the direct effect of piezoelectricity. The operating power of the smart sensor and the data used for damage identification is harvested directly from the sensing signal induced by a piezoelectric... Show more"This research proposes novel damage progression quantification and data robustness evaluation approaches, for structural health monitoring (SHM), using a new class of self-powered piezo-floating-gate (PFG) sensors. This system relies on harvesting the mechanical energy from structures through the direct effect of piezoelectricity. The operating power of the smart sensor and the data used for damage identification is harvested directly from the sensing signal induced by a piezoelectric transducer under dynamic loading. The developed models integrate structural simulations using finite element method (FEM) techniques, experimental studies, and statistical and artificial intelligence (AI) methods. In this work, the performance of the sensing system in identifying damage is investigated for various damage scenarios based on numerical and experimental studies. Both steel and pavement structures are studied. A new surface sensing approach for detecting bottom-up cracks in asphalt concrete (AC) pavement is proposed. Two types of self-powered wireless sensors are investigated in this research. Different data interpretation techniques are developed for each type of sensor. The data are obtained from finite element simulations, or experimental measurement, and are fitted to probability distributions to define initial damage indicators. Sensor fusion models are developed based on the concept of group-effect of sensors, in order to increase the damage detection resolution of individual sensors. Probabilistic neural network (PNN) and support vector machine (SVM) methods are used to improve the accuracy of the proposed damage identification methods for the case of multi-class damage progression. The proposed work is divided into four main parts: (i) Damage identification in steel structures using data from a uniform PFG sensor, (ii) Damage detection in steel and pavement structures using a non-uniform PFG sensor, (iii) Damage detection and localization in steel frame structures using hybrid network of self-powered strain and vibration sensors, and, (iv) a field demonstration of the new technology on the Mackinac Bridge in Michigan. The cases of the U10W gusset plate of the I-35W bridge in Minneapolis, MN, a steel girder, a steel plate under compaction tension mode, and an AC beam under three-point bending configuration are investigated. A surface sensing approach to detect bottom-up cracking in AC pavement under dynamic moving load is also proposed. This approach is based on interpreting the data of a surface-mounted network of sensors. Moreover, a hybrid network of strain and vibration-based sensors is used to detect damage in bolted steel frames. The objective is to establish a local-to-global strategy for damage identification in frames. Data fusion models combined with AI classifiers are developed. Uncertainty analysis is performed to verify the performance of the sensors under different noise levels."--Pages ii-iii. Show less
