The purpose of this dissertation is to demonstrate the ability to design health monitoring systems from a systematic perspective and how, with proper sensor and actuator placement, damage occurring in a structure can be detected and tracked. To this end, a design optimization was performed to determine the best locations to excite the structure and to collect data while using the minimum number of sensors. The type of sensors used in this design optimization was uni-axis accelerometers. It should be noted that the design techniques presented here are not limited to accelerometers. Instead, they allow for any type of sensor (thermal, strain, electromagnetic, etc.) and will find the optimal locations with respect to defined objective functions (sensitivity, cost, etc.). The use of model-based optimization techniques for the design of the monitoring system is driven by the desire to obtain the best performance possible from the system given what is known about the system prior to implementation. The use of a model is more systematic than human judgment and is able to take far more into account by using information about the dynamical response of a system than even an experienced structural engineer. It is understood in the context of structural modeling that no model is 100\% accurate and that any designs produced using model-based techniques should be tolerant to modeling errors. Demonstrations performed in the past have shown that poorly placed sensors can be very insensitive to damage development. To perform the optimization, a multi-objective genetic algorithm (GA) was employed. The objectives of the optimization were to be highly sensitive to damage occurring in potential “hot spots” while also maintaining the ability to detect damage occurring elsewhere in the structure and maintaining robustness to modeling errors. Two other objectives were to minimize the number of sensors and actuators used. The optimization only considered placing accelerometers, but it could have considered different type of sensors (i.e. strain, magneto-restrictive) or any combination thereof.

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<p dir="ltr">The research in this thesis focuses on developing an alert system to detect damage or impact on bridge. It employs Raspberry Pi and accelerometers for real-time health monitoring. The methodology includes bridge model creation, testing under no damage, impact, and structural damage conditions, and data processing for vibration frequency analysis. The aim is to differentiate between normal bridge conditions, collisions, and structural damages, providing timely notifications for necessary investigations or repairs. The study addresses the challenges in bridge safety and aims to improve maintenance efficiency and reliability.</p><p><br></p>

As the nation’s infrastructure continues to age, there is a growing need for methods to safely inspect critical structures, often during operation. The failure of post-tensioned anchor rods in Tainter style flood gates presented an immediate need for new inspection capabilities for U.S. Army Corps of Engineers (USACE) managed flood control gates. In response to this need, the Sensor Integration Branch (SIB) of The U.S. Army Engineer Research and Develop Center (ERDC) developed the capability to non-destructively test (NDT) both greased and grouted cylindrical steel anchor rods using higher order guided wave ultrasonic testing. Understanding the results requires a knowledge of both guided waves and digital signal processing in order to identify the possibility of a defect. In order to both facilitate rapid defect identification and expanding ease-of-use of the equipment, the research in this thesis uses a combination of the discrete wavelet transform (DWT) and gradient boosting machine learning to build a model capable of identifying the dispersive defect responses in the rods.

As transportation infrastructure continues to age, new methods of non-contact monitoring should be evaluated and, if found suitable, employed for bridge monitoring and structural health assessment. This study highlights the use of infrasound monitoring, a geophysical technique utilizing acoustics below 20 Hz, as one possible solution for non-contact, non-line-of-sight infrastructure health monitoring. This dissertation focuses on the technique of infrasound for infrastructure monitoring (bridges are of primary interest) beginning with a literature review and an overview of current operational considerations for infrasound for infrastructure monitoring developed at the U.S. Army Engineer Research and Development Center. A meta-analysis of bridge vibrational characteristics was completed following identification of a gap in the knowledge base in this area. This completed meta-analysis compared vibrational characteristics across multiple bridge types and construction materials to determine applicability of infrasound for detection and monitoring of each bridge type. With these considerations in mind, an experimental series involving a steel, two-girder bridge in northern California was completed using infrasound to detect natural modes of the structure and validated by on-structure accelerometers. The non-contact nature of this structural assessment approach has potential to supplement traditional structural assessment techniques as affordable, remote, persistent monitoring of transportation infrastructure. Upon completion of the original experimental series, the data were used to investigate the possibility of wide area monitoring using infrasound, including possible limitations and boundaries. Overall implications for use of this technology are also discussed for a multiple infrastructure types.

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