A baseline finite element model was developed for bridge management and calibration using nondestructive test data. The model calibration technique was evaluated on the Vernon Avenue Bridge over the Ware River in Barre, Massachusetts. This newly constructed bridge was instrumented throughout its construction phases in preparation for a static truck load test performed before the bridge opening. The strain data collected during the load test was used to calibrate a detailed baseline finite element model in an effort to represent the 3D system behavior of the bridge. Three methods of load ratings were used and compared: (1) conventional method, (2) conventional method updated by using NDT data, and (3) finite element model calibrated with NDT data.

Structural health monitoring (SHM) is a method for determining the structural integrity of civil infrastructure elements as a basis for maintenance and repair protocols. This monitoring depends on collecting structural response data from sensors installed on the structure due to in-service excitations. The installation additionally requires access to structural elements, power, and communication. New methods for remote measurement of displacements using video cameras could greatly simplify the process of instrumentation, making SHM much more attainable for many structures. This paper presents the remote camera measurement of the motions of the World War I Memorial Bridge in Portsmouth, New Hampshire, a vertical-lift bridge, from a distance of over 80 m. Vibration frequencies and mode shapes of the bridge are identified by measuring the displacements due to the lift span impact. Displacement of the bridge due to in-service traffic is also measured. Measured signals are compared with those from accelerometers and strain gauges installed on the bridge, and identified characteristics of the bridge are compared with a finite-element model for verification. Results show the potential of applying video cameras to measure and visualize vibrations of structures in SHM.

A Frequency Response Functions (FRFs)‐based two‐step algorithm to identify stiffness, mass, and viscous damping matrices is developed in this work. The proposed technique uses the difference between the experimentally recorded FRF and their analytical counterparts by minimizing the resultant error function at selected frequency points. In the first step, only mass and stiffness matrices are updated while keeping the uncalibrated viscous damping matrix constant. In the second step, the damping matrix is updated via changes on the selected unknown modal damping ratios. By using a stacking procedure of the presented error function that combines multiple data sets, adverse effects of noise on the estimated modal damping ratios are decreased by averaging the FRF amplitudes at resonant peaks. The application of this methodology is presented utilizing experimentally obtained data. The presented algorithm can perform an accurate structural identification via model updating, with a viscous damping matrix that captures the variation of the modal damping ratios with natural frequencies as opposed to other conventional proportional damping matrix formulations.

The vulnerability of our nation’s transportation infrastructure to climate change and extreme weather is now well documented and the transportation community has identified numerous strategies to potentially mitigate these vulnerabilities. The challenges to the infrastructure sector presented by climate change can only be met through collaboration between the climate science community, who evaluate what the future will likely look like, and the engineering community, who implement our societal response. To facilitate this process, the authors asked: what progress has been made and what needs to be done now in order to allow for the graceful convergence of these two disciplines? In late 2012, the Infrastructure and Climate Network (ICNet), a National Science Foundation–supported research collaboration network, was established to answer that question. This article presents examples of how the ICNet experience has shown the way toward a new generation of innovation and cross-disciplinary research, challenges that can be address by such collaboration, and specific guidance for partnerships and methods to effectively address complex questions requiring a cogeneration of knowledge.