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The Illinois Structural Health Monitoring Project was founded in 2002 when Professor Bill F. Spencer, director of the Smart Structures Technology Laboratory, and Professor Gul Agha, director of the Open Systems Laboratory, began a collaborative effort between the two laboratories at the University of Illinois at Urbana–Champaign.
The value of structural health information is the expected utility gain of a built environment system by information provided by structural health monitoring (SHM).The quantification of the value of structural health information is based on decision analysis adapted to built environment engineering.
The system is used for disaster control, structural health management and data analysis. There were a number of considerations before implementation which included: force (wind, earthquake, temperature, vehicles); weather (air temperature, wind, humidity and precipitation); and response (strain, acceleration, cable tension, displacement and tilt).
Structural health monitoring: Pines and Salvino [2002] applied HHT in structural health monitoring. Yang et al. [2004] used HHT for damage detection, applying EMD to extract damage spikes due to sudden changes in structural stiffness .
GitHub Copilot is an artificial intelligence model developed by GitHub and OpenAI that is able to autocomplete code in multiple programming languages. [355] Price for individuals: $10/mo or $100/yr, with one free month trial. Tabnine was created by Jacob Jackson and was originally owned by Tabnine company.
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses machine ethics and AI alignment, which aim to ensure AI systems are moral and beneficial, as well as monitoring AI systems for risks and enhancing their reliability.