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Bridges are vital components of the U.S. transportation network. However, every year, the transportation agencies report a large number of aging bridges that are structurally damaged. Also, evolving traffic and particularly the overloaded traversing traffic can threaten the bridges' integrity and safety further. Bridge weight-in-motion (BWIM) is a system that takes the instrumented bridges as a scale and uses the structure response to compute the trucks' weights with no interruption in the traffic. In a particular type of BWIM, called nothing-on-road BWIM (NOR-BWIM), only a few weighing sensors should be installed under the bridge top slab. Since nothing will be installed on the road surface, NOR-BWIM addresses some of the main challenges of pavement-based WIM and traditional BWIM systems. These include lane closure, interruption to the traveling traffic, and sensitivity to daily tire impacts and harsh weather conditions. It also provides a portable solution with a less labor-intense installation process. Additionally, previous studies have shown that BWIM systems are versatile candidates for overcoming the critical challenges of structural health monitoring (SHM) across various types of bridges. The integration of the two systems is more cost-effective with improved performance; thus, it is more attractive to practitioners. However, the current BWIMs have serious shortcomings that make the integrated SHM-BWIM systems impractical in real-world long-span bridges. In the first two phases of this study, these shortcomings are addressed and a novel BWIM system is proposed. Then, the novel BWIM system is used for SHM in the third phase of the study. These shortcomings are explained as follows. Most studies are performed on short/medium-span T-beam and slab-on-girder bridges. However, longer span lengths, construction methods, different slab properties (e.g., stiffness), etc., can affect the efficacy of the NOR-BWIM. Thus, there is a need to further evaluate this technique on other bridges, such as concrete-box-girder bridges with longer spans, in an effort to ascertain whether or not NOR-BWIM systems would still work effectively on such bridges. Thus, the first phase presents an experimental investigation conducted for a long-span concrete-box-girder bridge (144 m span) called the Smart Road bridge. A total of 18 experimental tests were performed on the bridge. Moreover, a cost-effective sensor placement was developed. It was found that the number of axles is detectable with an accuracy of 100%. Moreover, the estimated mean-absolute-error for axle spacing, vehicle speed, and gross vehicle weight were 4.6%, 2.6%, and 4.6%, respectively. Lastly, it was also demonstrated that the developed cost-effective NOR-BWIM system is capable of lane identification and truck position detection. The second main issue with the existing BWIM approaches is their limited suitability for simultaneous multiple-vehicle cases on multiple-lane bridges. To address this limitation, in the second phase of this study, a novel BWIM approach is proposed. The approach is built around the removal of the non-localized portion of the strain response. Keeping the localized portion of the strain response, which is not sensitive to nearby loads, allowing for enhanced detection. The superiority of this approach stems from its capability to handle multiple-vehicle cases. These may present with an arbitrary number of trucks and light-weight vehicles, simultaneously passing the bridge in any arbitrary pattern or configuration. To show the applicability of the approach, a finite element (FE) model of a long-span concrete-box-girder bridge was simulated. The model was validated against the experimental data collected under known large events. The FE model was then used to consider single-truck events (for proof-of-concept) as well as complex multiple-truck traffic cases. These included in-one-row trucks, zigzag patterns, side-by-side trucks, and a combination of several trucks with several light-weight vehicles present. The results demonstrated that the proposed BWIM approach is capable of detecting the axle weights and gross vehicle weight (GVW) of the traversing trucks. Based on all complex multiple-truck cases, the overall mean absolute errors for GVW and axle weight estimations were 4.5% and 11.3%, respectively. In the last phase, a multiple-presence dual-purpose (MPDP) SHM approach was proposed to monitor the integrity of bridges using the BWIM system existing sensors. This approach centers on the influence line (IL) change and uses a developed multiple-presence IL (MP-IL) technique (in the second phase) for SHM application. This can effectively handle the multiple presence issue of the current integrated SHM-BWIM systems to make them more practical. Also, unlike many SHM-BWIM studies, noise and transverse position change (defined as false damage indicators) were included in the proposed procedure to provide a more realistic bridge health monitoring approach. To show the applicability of the approach, a similar FE model simulated in the second phase was used. The model was then used to evaluate the MPDP approach under single and multiple truck events. Eleven damage scenarios were simulated, and three SHM trucks (a 3-axle, a 4-axle, and a 5-axle) were used to improve the SHM accuracy. Also, an updated sensor placement was proposed to effectively work for both BWIM and SHM applications in both single and multiple-truck events. According to the results, the MPDP SHM procedure coupled with the novel MP-IL and the proposed sensor placement could effectively detect the damage scenarios in both single and multiple-truck events. Also, it was shown that using several independent SHM trucks can make the monitoring process more effective. / Doctor of Philosophy / Every year, the transportation agencies report a large number of aging bridges that are structurally damaged. Also, evolving traffic and particularly overloaded traffic can threaten the bridges' integrity and safety further. Bridge weight-in-motion (BWIM) is a traffic system that takes the instrumented bridges as a scale and uses the structure response to compute the trucks' weights with no interruption in the traffic. In a particular type of BWIM, called nothing-on-road BWIM (NOR-BWIM), only a few weighing sensors should be installed under the road surface. Since nothing will be installed on the road surface, NOR-BWIM addresses some of the main challenges of pavement-based WIM and traditional BWIM systems. These include lane closure, interruption to the traveling traffic, and sensitivity to daily tire impacts and harsh weather conditions. It also provides a portable solution with a less labor-intense installation process. Additionally, previous studies have shown that BWIM systems are versatile candidates for overcoming the critical challenges of structural health monitoring (SHM) across various types of bridges. The integration of the two systems is more attractive to practitioners because it brings improved performance at a lower cost. However, the current BWIMs have serious shortcomings that make the integrated SHM-BWIM systems impractical in real-world long-span bridges. In the first two phases of this study, these shortcomings are addressed and a novel BWIM system is proposed. Then, the novel BWIM system is used for SHM in the third phase of the study. These shortcomings are explained as follows. Most studies are performed on short/medium-span bridges with particular types of structures. However, longer span lengths, construction methods, different bridge components' properties, etc., can affect the efficacy of the NOR-BWIM. Thus, there is a need to further evaluate this technique on other bridges with longer spans and different structural systems to ascertain whether or not NOR-BWIM systems would still work effectively on such bridges. Thus, the first phase presents an experimental investigation conducted for a long-span concrete-box-girder bridge (a different structural system than the literature) with 144-m spans. A total of 18 experimental tests were performed on the bridge. Moreover, a cost-effective sensor placement was developed. It was found that the number of axles is detectable with no error. Moreover, the estimated error for axle spacing, vehicle speed, and gross vehicle weight were all low. Lastly, it was also demonstrated that the developed cost-effective NOR-BWIM system is capable of lane identification and truck position detection. The second main issue with the existing BWIM approaches is their limited suitability for simultaneous multiple vehicles on multiple-lane bridges. To address this limitation, in the second phase of this study, a novel BWIM approach is proposed. The superiority of this approach stems from its capability to handle multiple-vehicle cases. These may present with an arbitrary number of trucks and light-weight vehicles, simultaneously passing the bridge in any arbitrary pattern or configuration. To show the applicability of the approach, a model of the long-span bridge was simulated. The model was validated against the experimental data collected under known traffic events. The model was then used to consider single-truck events and complex multiple-truck traffic cases. The results demonstrated that the proposed BWIM approach can detect the axle weights and gross vehicle weight (GVW) of the traversing trucks. Based on all complex multiple-truck cases, the overall errors for GVW and axle weight estimations were 4.5% and 11.3%, respectively. In the last phase, a novel SHM approach was proposed to monitor the integrity of bridges using the existing sensors for BWIM. This approach uses the proposed BWIM system for SHM application. This can effectively handle the multiple presence issue of the current integrated SHM-BWIM systems to make them more practical. Also, unlike many SHM-BWIM studies, noise and transverse position change were included in the proposed procedure to provide a more realistic bridge health monitoring approach. A similar model simulated in the second phase was used to show the applicability of the approach. The model was then used to evaluate the MPDP approach under single and multiple truck events. Eleven damage scenarios were simulated. Also, an updated sensor placement was proposed to work effectively for both BWIM and SHM applications in single and multiple-truck events. According to the results, the proposed SHM procedure coupled with the novel BWIM and the proposed sensor placement could effectively detect the damage scenarios in both single and multiple-truck events.

With regard to the space field, the number of the sensors has grown for a middle-sized spacecraft from more than 500 at the beginning of the twenty-first century [1] to several thousands for nowadays applications. Meanwhile, Reusable Launch Vehicles (RLVs) moved their steps from demonstrators to commercial working systems. As a result, Health Monitoring (HM) is conquered its own space in the field and sensors are the primary elements required for implementing a monitoring unit. The innovative concept of reusable rockets requires, from the point of view of HM implementation, not only the evaluation of the vehicle health status but also the prediction of the reusability of the individual subsystems w.r.t. the next launch cycle. Therefore, the goal of this work is divided in two parts. The former is to identify the most critical points for the development of reusable rockets, focusing on theoretical working conditions and analysis or failures. The latter is to discuss the sensing units useful for addressing the defined points, describing the possible innovative approaches for sensing the system conditions. Among them, piezoelectric units, fiber optics, imaging units, and conductive layers can be identified for enhancing the comprehension of the system working conditions.

Structural Health Monitoring is a useful tool for reducing maintenance costs and improving the life and performance of engineering structures. Impedance-Based SHM utilizes the coupled electromechanical behavior of piezoelectric materials to detect adverse changes and material and mechanical failures of structures. Environmental variables such as temperature present a challenge to assessing the veracity of damage detected through statistical modeling of impedance signals. An effective frequency shift method was developed to compensate impedance measurements for changes resulting from environmental temperature fluctuations. This thesis investigates how the accuracy of this method can be improved and be applied to a 100oF range of temperatures. Building up the idea of eliminating temperature effects from impedance measurements, this thesis investigates the possibility of using statistical moments to create a temperature independent impedance baseline. / Master of Science

Structural Health Monitoring (SHM) of automobile tires has been an active area of research in the last few years. Within this area, the monitoring of strain on tires using wireless devices and networks is gaining prominence because these techniques do not require any wired connections. Various tire manufacturers are looking into SHM of automobile tires due to the Transportation Recall Enhancement, Accountability and Documentation (TREAD) act which demands installation of tire pressure monitoring devices within the tire. Besides measuring tire pressures, tire manufactures are also examining ways to measure strain and temperature as well to enhance overall safety of an automobile. A sensor system that can measure the overall strain of a tire is known as a centralized strain sensing system. However, a centralized strain sensing system cannot find the location and severity of the damage on the tire, which is a basic requirement. Various sensors such as acceleration and optical sensors have also been proposed to be used together to get more local damage information on the tire. In this thesis we have developed a strain sensing system that performs local strain measurements on the tire and transmits them to a console inside the vehicle wirelessly. Our sensing system utilizes a new sensing material called Metal RubberTM which is shown to be conductive like metal, and flexible as rubber. Also, we have also developed a reliable and an energy efficient geographic routing protocol for transporting strain data wirelessly from a tire surface to the driver of the automobile. / Master of Science

This work describes a comparison of biocompatible membranes to encapsulate gas sensors in the human intestinal tract. The goal is to identify semipermeable materials that allow intestinal gases to pass through the membrane while also separating them from other intestinal contents. Important aspects of this study are the membranes’ chemical resistance to gastric acid and their gas permeability. Chemical resistance was assessed by submerging selected membranes in artificial gastric acid. Surface structure of membrane samples before and after the acid treatment was compared using scanning electron microscopy and signs of degradation were documented. To characterize the membranes in terms of their gas permeability, an experimental setup was developed to measure average airflow through the membranes. A constant pressure of 7 kPa was applied across the membrane and flow rate Q of air was measured over a predefined surface area A. This evaluation marks the first step in the development of an ingestible capsule for in-vivo analysis of intestinal gases.

Civil infrastructure throughout the world serves as main arteries for commerce and transportation, commonly forming the backbone of many societies. Bridges have been and remain a crucial part of the success of these civil networks. However, the crucial elements have been built over centuries and have been subject to generations of use. Many current bridges have outlived their intended service life or have been retrofitted to carry additional loads over their original design. A large number of these historic bridges are still in everyday use and their condition needs to be monitored for public safety. Transportation infrastructure authorities have implemented various inspection and management programs throughout the world, mainly visual inspections. However, careful visual inspections can provide valuable information but it has limitations in that it provides no actual stress-strain information to determine structural soundness. Structural Health Monitoring (SHM) has been a growing area of research as officials need to asses and triage the aging infrastructure with methods that provide measurable response information to determine the health of the structure. A rapid improvement in technology has allowed researchers to start using new sensors and algorithms to understand the structural parameters of tested structures due to known and unknown loading scenarios. One of the most promising methods involves the use of wireless sensor nodes to measure structural responses to loads in real time. The structural responses can be processed to help understand the modal parameters, determine the health of the structure, and potentially identify damage. For example, modal parameters of structures are typically used when designing the lateral system of a structure. A better understanding of these parameters can lead to better and more efficient designs. Usually engineers rely on a finite element analysis to identify these parameters. By observing the actual parameters displayed during field testing, the theoretical FE models can be validated for accuracy. This paper will present the field testing of a historic wrought iron truss bridge, in a case study, to establish a repeatable procedure to be used as reference for the testing of other similar structures.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / While any vehicle that is typically part of a flight test campaign is heavily instrumented to validate its performance, long term vehicle health monitoring is performed by a significantly reduced number of sensors due to a number of issues including cost, weight and maintainability. The development and deployment of smart sensor buses has reached a time in which they can be integrated into a larger data acquisition system environment. The benefits of these types of buses include a significant reduction in the amount of wiring and overall system complexity by placing the appropriate signal conditioners close to their respective sensors and providing data back over a common bus, that also provides a single power source. The use of a smart-sensor data collection bus, such as IntelliBus™1 or IEEE-1451, along with the continued miniaturization of signal conditioning devices, leads to the interesting possibility of permanently embedding data collection capabilities within a vehicle after the initial flight test effort has completed, providing long-term health-monitoring and diagnostic functionality that is not available today. This paper will discuss the system considerations and the benefits of a smart sensor based system and how pieces can be transitioned from flight qualification to long-term vehicle health monitoring in production vehicles.