Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.

Mannheimia haemolytica is the principal bacterium isolated from respiratory disease in feedlot cattle and is a significant component of enzootic pneumonia in all neonatal calves. A commensal of the nasopharynx, M. haemolytica is an opportunist, gaining access to the lungs when host defenses are compromised by stress or infection with respiratory viruses or mycoplasma. Although several serotypes act as commensals, A1 and A6 are the most frequent isolates from pneumonic lungs. Potential virulence factors include adhesin, capsular polysaccharide, fimbriae, iron-regulated outer membrane proteins, leukotoxin (Lkt), lipopolysaccharide (LPS), lipoproteins, neuraminidase, sialoglycoprotease and transferrin-binding proteins. Of these, Lkt is pivotal in induction of pneumonia. Lkt-mediated infiltration and destruction of neutrophils and other leukocytes impairs bacterial clearance and contributes to development of fibrinous pneumonia. LPS may act synergistically with Lkt, enhancing its effects and contributing endotoxic activity. Antibiotics are employed extensively in the feedlot industry, both prophylactically and therapeutically, but their efficacy varies because of inconsistencies in diagnosis and treatment regimes and development of antibiotic resistance. Vaccines have been used for many decades, even though traditional bacterins failed to demonstrate protection and their use often enhanced disease in vaccinated animals. Modern vaccines use culture supernatants containing Lkt and other soluble antigens, or bacterial extracts, alone or combined with bacterins. These vaccines have 50-70% efficacy in prevention of M. haemolytica pneumonia. Effective control of M. haemolytica pneumonia is likely to require a combination of more definitive diagnosis, efficacious vaccines, therapeutic intervention and improved management practices.

Wireless smart sensors enable new approaches to improve structural health monitoring (SHM) practices through the use of distributed data processing. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of fullscale implementations due to the lack of critical hardware and software elements. This research develops a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. The framework software is based on a service-oriented architecture that is modular, reusable and extensible, thus allowing engineers to more readily realize the potential of smart sensor technology. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. The framework developed in this research has been validated on a full-scale a cable-stayed bridge in South Korea.

Industrialized nations have a huge investment in the pervasive civil infrastructure on which our lives rely. To properly manage this infrastructure, its condition or serviceability should be reliably assessed. For condition or serviceability assessment, Structural Health Monitoring (SHM) has been considered to provide information on the current state of structures by measuring structural vibration responses and other physical phenomena and conditions. Civil infrastructure is typically large-scale, exhibiting a wide variety of complex behavior; estimation of a structure's state is a challenging task. While SHM has been and still is intensively researched, further efforts are required to provide efficient and effective management of civil infrastructure. Efforts toward realization of SHM systems using smart sensors, however, have not resulted in full-fledged applications. All efforts to date have encountered difficulties originating from limited resources on smart sensors (e.g., small memory size, small communication throughput, limited speed of the CPU, etc.). To realize an SHM system employing smart sensors, this system needs to be designed considering both the characteristics of the smart sensor and the structures to be monitored.This research addresses issues in smart sensor usages in SHM applications and realizes, for the first time, a scalable and extensible SHM system using smart sensors. The iv architecture of the proposed SHM is first presented. The Intel Imote2 equipped with an accelerometer sensor board is chosen as the smart sensor platform to demonstrate the efficacy of this architecture. Middleware services such as model-based data aggregation, reliable communication, and synchronized sensing are developed. SHM Algorithms identified as promising for the usage on smart sensor systems are extended to improve practicability and implemented on Imote2s. Careful attention has been paid to integrating these software components so that the system possesses identified desirable features.The damage detection capability and autonomous operation of the developed system are then experimentally verified. The SHM system consisting of ten Imote2s are installed on a scale-model truss. The SHM system monitors the truss in a distributed manner to localize simulated damage.In summary, this thesis proposes and realizes a scalable and autonomous SHM system using smart sensors. The system is experimentally verified to be effective for damage

No abstract