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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. [ABSTRACT FROM AUTHOR]

After an explosion, determining the remaining capacity of a structure to resist a progressive collapse can provide valuable information for emergency operations and decision makers. Condition assessments after a blast are commonly performed with a visual inspection. However, visual inspections can be time-consuming and involve putting additional personnel into harm's way. Analytical blast analyses can estimate a structures post-blast condition, but to achieve a high level of accuracy these analyses can be time-consuming and require information regarding the blast event which may not be available at the time. This presents a need for a threat independent post-blast analysis method, which can assess for the post-blast structural condition without requiring personnel to enter the structure. The alternate path method has been used to design buildings to resist a progressive collapse; however, it does not incorporate damage outside of element failure making it unsuitable for post-blast condition assessments. Model updating using modal properties from vibration measurements has been used in structural health monitoring to estimate damage on structures, but it has not been applied to post-blast structures. In this article, a method to estimate the remaining elemental structural capacity of a post-blast structure, called the adaptive alternate path analysis, is presented. This method involves using the alternate path method to assess an updated numerical model, which incorporates the buildings' structural damage. To demonstrate the impact of incorporating additional damage beyond elemental failure on a structure's capacity, a simulated study is presented using simulated stiffness reductions. A blast simulation is then used to show the capacity of the updated numerical model to represent the post-blast structure and the improvements gained over using the original model. The presented methodology can be used to assess a structures potential for progressive collapse after a blast, leading to safer emergency operations. [ABSTRACT FROM AUTHOR]

After hazardous events, it is important to be able to quickly identify the remaining stiffness of affected structures for condition evaluation. Model updating can be used to update structural models to reflect current conditions based upon experimental measurements. Direct model updating is a simple and quick method of damage detection, but does not guarantee physical relevance. Least-squares optimization can be used to accurately identify damage with physical relevance, but needs more measurements then updating parameters in order to produce an accurate solution. However, after an extreme event sensors on the structure may be damaged, creating a scenario with limited measurements which can render optimization techniques incapable of assessing the remaining stiffness. To address this issue, this paper proposes a two-phase method to localize and then quantify the remaining stiffness of the structure. Direct model updating with limited measurements is used to localize potential damage to a subset of parameters, and a least-squares optimization using the localized parameters is used to quantify the remaining stiffness in the structure. Numerical simulations using a simplified model based upon the phase I IASCE-ASCE structural health monitoring benchmark problem with missing first floor sensors have been employed to demonstrate, and experiments using a five-story steel frame structure are conducted to validate the methodology. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]