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1. Real-Time Distributed Cloud Computing Architecture for Structural Health Monitoring

   https://doi.org/10.1061/9780784482230.005  

   Searls, Owen; Zhao, Zhiyong; Couch, Alva L.; Sanayei, Masoud (April 2019, Structures Congress Conference 2019)

   Real-time fatigue health monitoring has the potential to serve as a valuable complement to structural health monitoring (SHM) for bridge inspections. SHM is an objective supplement to visual bridge inspections with a minimum interval between bridge inspections at 24 months. SHM can provide quantitative and objective data on a bridge’s fatigue condition for fracture-critical components, of which fatigue is a criterion. Current methods of continuous structural health monitoring for condition assessment are performed by collecting measured bridge response subjected to operational traffic from an array of sensors installed on fracture-critical members of a bridge. The measured responses are used to determine the remaining fatigue life of the bridge—the minimum time before repair. The large amount of data involved in this process complicates the design of a system that will automate the data collection process at a bridge, analyze that data, and display information about bridge health to researchers and engineers. Variations in bridge designs and condition assessment algorithms also necessitate that such a system be modular and adaptable to allow for expansion to additional structures. A new system has been developed that separates bridge SHM from the data storage and communication system. This architecture creates a reliable interface for sending data from one or more bridges to a cloud server where it can be processed using modular algorithms that can be adapted for different use cases. The cloud-based web service and data repository makes bridge structural health data available to researchers at all steps of the process. This system provides significant advantages over previous platforms for structural health monitoring and condition assessment, most notably in the areas of modularity, extensibility, and reliability. 

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2. Smart Moves: Making Sense of the Math in Environmental Data

   Merson, Martha; Gonzalez-Carrillo, Selene; Contini-Field, Ethan; Small, Meredith (July 2016, Science education & civic engagement)

   Environmental organizers and their constituents, local community group members concerned about environmental health, operate in a context with rich and varied opportunities for learning about and applying mathematics to communicating environmental data. Prior to Statistics for Action, project partners—organizers at environmental non-profits—spent little time with group members analyzing data. Organizations did not have a method or protocol for considering the most effective way to frame findings for neighbors and decision makers. During the Statistics for Action Project, STEM educators and environmental organizers collaborated to use the context of environmental organizing as a platform for science and math learning. This article describes Smart Moves and Memorable Messages, two approaches that advanced goals for both math learning and organizing 

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3. Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare

   https://doi.org/10.1038/s41746-023-00965-x  

   Oniani, David; Hilsman, Jordan; Peng, Yifan; Poropatich, Ronald K.; Pamplin, Jeremy C.; Legault, Gary L.; Wang, Yanshan (December 2023, npj Digital Medicine)

   Abstract In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the “GREAT PLEA” ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Autonomy for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice. 

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4. Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

   https://doi.org/10.3390/s20102778  

   Azimi, Mohsen; Eslamlou, Armin; Pekcan, Gokhan (May 2020, Sensors)

   Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications. 

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5. An Intelligently Designed AI for Structural Health Monitoring of a Reinforced Concrete Bridge

   Locke, William R; Mokalled, Stefani C; Abuodeh, Omar R; Redmond, Laura M; McMahan, Christopher S (January 2021, The Concrete Industry in the Era of AI)

   e. With recent advances in online sensing technology and high-performance computing, structural health monitoring (SHM) has begun to emerge as an automated approach to the real-time conditional monitoring of civil infrastructure. Ideal SHM strategies detect and characterize damage by leveraging measured response data to update physics-based finite element models (FEMs). When monitoring composite structures, such as reinforced concrete (RC) bridges, the reliability of FEM based SHM is adversely affected by material, boundary, geometric, and other model uncertainties. Civil engineering researchers have adapted popular artificial intelligence (AI) techniques to overcome these limitations, as AI has an innate ability to solve complex and ill-defined problems by leveraging advanced machine learning techniques to rapidly analyze experimental data. In this vein, this study employs a novel Bayesian estimation technique to update a coupled vehicle-bridge FEM for the purposes of SHM. Unlike existing AI based techniques, the proposed approach makes intelligent use of an embedded FEM model, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM model and the results are compared against the results obtained for “true” parameter values. A sensitivity study is also conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when leveraging experimental data to update FEMs of concrete structures using AI techniques. 

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