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Advancing RTHS methods to readily handle multi-dimensional problems has great potential for enabling more advanced testing and synergistically using existing laboratory facilities that have the capacity for such experimentation. However, the high internal coupling between hydraulics actuators and the nonlinear kinematics escalates the complexity of actuator control and boundary condition tracking. To enable researchers in the RTHS community to develop and compare advanced control algorithms, this paper proposes a benchmark control problem for a multi-axial real-time hybrid simulation (maRTHS) and presents its definition and implementation on a steel frame excited by seismic loads at the base. The benchmark problem enables the development and validation of control techniques for tracking both translation and rotation degrees of freedom of a plant that consists of a steel frame, two hydraulic actuators, and a steel coupler with high stiffness that couples the axial displacements of the hydraulic actuators resulting in the required motion of the frame node. In this investigation, the different components of this benchmark were developed, tested, and a set of maRTHS were conducted to demonstrate its feasibility in order to provide a realistic virtual platform. To offer flexibility in the control design process, experimental data for identification purposes, finite element models for the reference structure, numerical, and physical substructure, and plant models with model uncertainties are provided. Also, a sample example of an RTHS design based on a linear quadratic Gaussian controller is included as part of a computational code package, which facilitates the exploration of the tradeoff between robustness and performance of tracking control designs. The goals of this benchmark are to: extend existing control or develop new control techniques; provide a computational tool for investigation of the challenging aspects of maRTHS; encourage a transition to multiple actuator RTHS scenarios; and make available a challenging problem for new researchers to investigate maRTHS approaches. We believe that this benchmark problem will encourage the advancing of the next-generation of controllers for more realistic RTHS methods. 

The Federal Highway Administration (FHWA) mandates biannual bridge inspections to assess the condition of all bridges in the United States. These inspections are recorded in the National Bridge Inventory (NBI) and the respective state’s databases to manage, study, and analyze the data. As FHWA specifications become more complex, inspections require more training and field time. Recently, element-level inspections were added, assigning a condition state to each minor element in the bridge. To address this new requirement, a machine-aided bridge inspection method was developed using artificial intelligence (AI) to assist inspectors. The proposed method focuses on the condition state assessment of cracking in reinforced concrete bridge deck elements. The deep learning-based workflow integrated with image classification and semantic segmentation methods is utilized to extract information from images and evaluate the condition state of cracks according to FHWA specifications. The new workflow uses a deep neural network to extract information required by the bridge inspection manual, enabling the determination of the condition state of cracks in the deck. The results of experimentation demonstrate the effectiveness of this workflow for this application. The method also balances the costs and risks associated with increasing levels of AI involvement, enabling inspectors to better manage their resources. This AI-based method can be implemented by asset owners, such as Departments of Transportation, to better serve communities. 

The purpose of a routine bridge inspection is to assess the physical and functional condition of a bridge according to a regularly scheduled interval. The Federal Highway Administration (FHWA) requires these inspections to be conducted at least every 2 years. Inspectors use simple tools and visual inspection techniques to determine the conditions of both the elements of the bridge structure and the bridge overall. While in the field, the data is collected in the form of images and notes; after the field work is complete, inspectors need to generate a report based on these data to document their findings. The report generation process includes several tasks: (1) evaluating the condition rating of each bridge element according to FHWA Recording and Coding Guide for Structure Inventory and Appraisal of the Nation’s Bridges; and (2) updating and organizing the bridge inspection images for the report. Both of tasks are time-consuming. This study focuses on assisting with the latter task by developing an artificial intelligence (AI)-based method to rapidly organize bridge inspection images and generate a report. In this paper, an image organization schema based on the FHWA Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation’s Bridges and the Manual for Bridge Element Inspection is described, and several convolutional neural network-based classifiers are trained with real inspection images collected in the field. Additionally, exchangeable image file (EXIF) information is automatically extracted to organize inspection images according to their time stamp. Finally, the Automated Bridge Image Reporting Tool (ABIRT) is described as a browser-based system built on the trained classifiers. Inspectors can directly upload images to this tool and rapidly obtain organized images and associated inspection report with the support of a computer which has an internet connection. The authors provide recommendations to inspectors for gathering future images to make the best use of this tool. 

Collecting massive amounts of image data is a common way to record the post-event condition of buildings, to be used by engineers and researchers to learn from that event. Key information needed to interpret the image data collected during these reconnaissance missions is the location within the building where each image was taken. However, image localization is difficult in an indoor environment, as GPS is not generally available because of weak or broken signals. To support rapid, seamless data collection during a reconnaissance mission, we develop and validate a fully automated technique to provide robust indoor localization while requiring no prior information about the condition or spatial layout of an indoor environment. The technique is meant for large-scale data collection across multiple floors within multiple buildings. A systematic method is designed to separate the reconnaissance data into individual buildings and individual floors. Then, for data within each floor, an optimization problem is formulated to automatically overlay the path onto the structural drawings providing robust results, and subsequently, yielding the image locations. The end-to end technique only requires the data collector to wear an additional inexpensive motion camera, thus, it does not add time or effort to the current rapid reconnaissance protocol. As no prior information about the condition or spatial layout of the indoor environment is needed, this technique can be adapted to a large variety of building environments and does not require any type of preparation in the postevent settings. This technique is validated using data collected from several real buildings. 

Image data remains an important tool for post-event building assessment and documentation. After each natural hazard event, significant efforts are made by teams of engineers to visit the affected regions and collect useful image data. In general, a global positioning system (GPS) can provide useful spatial information for localizing image data. However, it is challenging to collect such information when images are captured in places where GPS signals are weak or interrupted, such as the indoor spaces of buildings. The inability to document the images’ locations hinders the analysis, organization, and documentation of these images as they lack sufficient spatial context. In this work, we develop a methodology to localize images and link them to locations on a structural drawing. A stream of images can readily be gathered along the path taken through a building using a compact camera. These images may be used to compute a relative location of each image in a 3D point cloud model, which is reconstructed using a visual odometry algorithm. The images may also be used to create local 3D textured models for building-components-of-interest using a structure-from-motion algorithm. A parallel set of images that are collected for building assessment is linked to the image stream using time information. By projecting the point cloud model to the structural drawing, the images can be overlaid onto the drawing, providing clear context information necessary to make use of those images. Additionally, components- or damage-of-interest captured in these images can be reconstructed in 3D, enabling detailed assessments having sufficient geospatial context. The technique is demonstrated by emulating post-event building assessment and data collection in a real building.