Ultra-high-performance concrete (UHPC) is a novel material with multiple known uses and many still yet to be discovered. Recently, the use of encasing welded shear studs in UHPC on the web of corroded steel beams was developed. This creates a bearing force transfer mechanism to bypass the corroded web plate. This new material and its uses come with many uncertainties in the short and long term. Structural health monitoring (SHM) can be a tool to observe the development. Specifically, radio frequency technology (RFID) can be used. RFID has existed commercially since the 1960s and has been used as a crack sensor before, but never with UHPC. RFID-based crack sensing is being used to monitor the UHPC retrofit. A crack is simulated on the UHPC specimen and then a commercial, low cost tag is secured. Using backscatter power, the tag reads the crack existence and its increasing volume with every new damage stage. Using a damage index, comparing the uncracked and each cracked stage, this method is not restricted to the raw received signal strength indicator (RSSI), which could be different at each tag. With this sensor, the small cracks that occur in UHPC during its creation can be monitored to ensure the capacity of the retrofitting is maintained. The tested RFID-based crack sensor can be used on various other forms of UHPC.

To meet the growing demands to monitor our aging infrastructure, wireless smart sensor networks (WSSN) have been the subject of intense interest due to their versatility and low cost. However, the performance of commercially available sensors is not sufficient to realize the high-fidelity data required for SHM. In particular, synchronization among the wireless sensor nodes has been found to be inadequate for data intensive applications such as SHM. To this end, the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite has been developed as an open-source framework to enable reliable SHM application. One of the services, RemoteSensing, was specifically designed to enable monitoring of civil infrastructure through accurate time synchronization and reliable communication. In this paper, the performance of the Imote2 wireless sensor platform using a commercially available low cost sensor board is validated directly against a wired sensor system, in the context of validation of its capability for high-fidelity data measurement for bridge health monitoring. A series of vibration tests have been conducted using human jumping to excite the subject of this study, a 76-m historic steel truss bridge in Mahomet, Illinois. The dynamic properties of the bridge have been obtained by the peak picking method for both wireless and wired systems and compare well; thus, demonstrating the efficacy of the wireless sensor system.

A passive radio frequency identification (RFID)-based crack sensor was developed and its sensitivity to crack propagation on metallic plates has been experimentally validated. A crack propagation test method was designed to evaluate any backscatter power-based crack sensor to monitor propagating cracks on a metallic surface. This new method uses plate specimens designed for standard fracture toughness testing. Systematic increases in displacement with a hydraulic universal test machine to propagate the crack were performed. Using digital imagery, the physical characteristics of the crack and fracture mode were determined with high precision. Linear elastic fracture was induced so as to more closely resemble the fracture mode that is to be expected in high strength steel bridge girders. The sensor was tested as a single unit and in various arrays of multiple units for increased pervasiveness in monitoring. A piecewise relationship between the damage index and crack opening was found. This relationship is a significant finding that demonstrates that RFID-based crack sensing is a feasible tool for bridge inspection.

Bridges are critical infrastructures that support our economic activities and daily lives. Aging bridges have been a major issue for decades, prompting researchers to improve resilience and performance through structural health monitoring. While most research focuses on superstructure damage, the majority of bridge failures are associated with support or joint damages, indicating the importance of bridge support. Indeed, bridge support affects the performance of both the substructure and superstructure by maintaining the load path and allowing certain movements to mitigate thermal and other stresses. The support deterioration leads to a change in fixity in the superstructure, compromising the bridge’s integrity and safety. Hence, a reliable method to determine support fixity level is essential to detecting bearing health and enhancing the accuracy of the bridge health monitoring system. However, such research is lacking because of its complexity. In this study, we developed a support fixity quantification method based on thermal responses using an Artificial Neural Network (ANN) model. A finite element (FE) model of a representative highway bridge is used to derive thermal displacement data under different bearing stiffnesses, superstructure damage, and thermal loading. The thermal displacement behavior of the bridge under different support fixity conditions is presented, and the model is trained on the simulated response. The performance of the developed FE model and ANN was validated with field monitoring data collected from two in-service bridges in Connecticut using a real-time Wireless Sensor Network (WSN). Finally, the support stiffnesses of both bridges were predicted using the ANN model for validation.