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  1. The COVID-19 era has witnessed numerous successful and unsuccessful attempts to adapt or reconfigure physical, virtual, and hybrid aspects of the built environment in order to mitigate the risks of co-occuring (i.e., compound) hazards. But it has also witnessed major challenges to ensuring that the protections these reconfigurations afford are equitably distributed. Additional theoretical and empirical research is needed to inform transitions (via adaptive reconfiguration) toward short-term goals of health and well-being, as well as to guide transformations (via the establishment of stable configuration) toward longer-term goals of equitable societal function. To this end, this paper presents a framework for conceptualizing adaptation of the built environment as a series of state transitions in response to (or in anticipation of) compound hazards. It draws upon cases from recent experience in the areas of food production, shelter, and education to critique, clarify, and explicate this framework. It concludes with implications for further research on the management of transitions in the built environment under a range of hazard scenarios. 
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  2. Technology transfer entails the systematic transference of scientific research results to practical tasks. The research product may be a novel design, an effective process, a tool or a set of tools. Effective technology transfer depends on many factors. It includes recognizing a gap in knowledge, focusing on the end user’s needs, long-term planning, effective communication and collaboration between researchers, standards organizations, and potential users, and a successful reduction of the knowledge or training burden required by the user. This Research Topic provides five examples of robust technology transfer from researchers seeking to mitigate the effect of natural hazards on the built and natural environment—transfers of knowledge that will significantly advance our nation’s resilience in the face of growing natural hazard threats. In 2016, the National Science Foundation established the Natural Hazards Engineering Research Infrastructure (NHERI) network. NHERI provides engineering and social science researchers with access to a world-class research infrastructure to support their efforts to improve the resilience and sustainability of the nation’s civil, natural and social infrastructure against earthquakes, windstorms and associated natural hazards such as tsunami and storm surge in coastal areas. Supported by the National Science Foundation, NHERI is a nation-wide network that consists of 12 university-based, shared-use experimental facilities, a computational modeling and simulation center, and a shared community cyber-infrastructure. 
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  3. The 2022 Natural Hazards Research Summit drew researchers, practitioners, and federal agency representatives together to reflect on the accomplishments achieved by the Natural Hazards Engineering Research Infrastructure (NHERI) community and to chart the path for the next decade of impactful natural hazards research. Convened in October, 2022 in Washington, D.C. with support from the National Science Foundation, the specific goals of the two-day Summit were to: (i) discuss and elucidate the research needs for the next 10 years, (ii) foster connections between the broader natural hazards community, and (iii) disseminate information on the resources and capabilities that NHERI offers to researchers focused on preventing natural hazards from becoming societal disasters. This report documents the findings and recommendations from the panel, town hall sessions, and visioning activities that took place at the Summit. The intended audience for the report is the natural hazards research community and the funding agencies that support its research. Accordingly, the report includes a research agenda developed with input from the Summit participants. 
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  4. 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. 
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  5. This is the third edition of the NSF Science Plan for the Natural Hazards Engineering Research Infrastructure (NHERI). It was developed to focus natural hazards research on some of the major challenges communities face as they work to enhance their resilience to natural hazard events. It provides information for researchers, funding agencies, practitioners, students, and the public on the critical research needs and the process of conducting multi-hazard research to advance hazards engineering practice and community resilience. The Science Plan provides Grand Challenges and Key Research Questions. 
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  6. 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. 
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  7. Summary

    The automated identification of building characteristics for seismic vulnerability remains a challenge for governments due to the high number of buildings in cities. The diverse architectural styles of these buildings complicate the automated identification of building information (e.g., number of stories, structural system, and material type). Deep learning techniques lose accuracy as they generalize information, while the visual contents of a building exhibit a considerable range and diversity. This study leverages the pose detection technique to tackle such issues by focusing on a common construction style: reinforced concrete buildings representing columns, beams, or floors on the façade. With an aim to enable the assessment of seismic vulnerability, the technique developed herein is conceived for buildings with up to six stories that are more likely to be moment‐frame buildings. The AI‐enabled proposed framework starts with collecting building images and categorizing those containing this specific building type. A bounding box detector is then used to isolate building facades, for the subsequent identification of the structural frame with the High‐Resolution Network (HR‐Net). For demonstration, we illustrate this technique by identifying the structural frame on concrete buildings with a sample dataset developed based on buildings found in Mexico City in a pre‐earthquake event state.

     
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