Page 15: Research news on AI-enabled digital twins

AI-enabled digital twins combine high-fidelity virtual replicas of physical assets with machine learning and sensing technologies to monitor condition, predict failures, and support operational decision-making. Applications span bridges, railways, nuclear reactors, wind turbines, manufacturing equipment, and urban infrastructure, integrating structural health monitoring, non-destructive evaluation, and high-resolution imaging. Data-driven models enable real-time fault diagnosis, risk-informed maintenance, and optimization of performance, often incorporating robotics, remote sensing, and time-series domain adaptation for robust, continuous infrastructure management.

Automotive

Through smartphone apps, AI can close road assessment gap

Dodging potholes is a familiar routine for drivers. But, behind every bump and crack in the pavement is a bigger issue: many communities lack the tools and data they need to maintain their roads effectively. Traditional pavement ...

Hi Tech & Innovation

Researcher develops generative learning model to predict falls

In a study published in the journal Information Systems Research, Texas Tech University's Shuo Yu and his collaborators developed a generative machine learning model to detect instability before a fall occurs. The hope is ...

Energy & Green Tech

AI model shortens the development time of new materials

Time-consuming testing and computer simulations are bottlenecks in the design of new materials. A thesis from the University of Gothenburg aims to develop an AI model that can efficiently determine the durability and strength ...

Engineering

New report on importance of measurement of engineering

A new report launched today by the Institution of Mechanical Engineers (IMechE) and the National Physical Laboratory (NPL) emphasizes the critical role of measurement in engineering. As engineers drive innovation and commercialize ...

Robotics

Tiny robots could help fix leaky water pipes

Micro-robots that can inspect water pipes, diagnose cracks and fix them autonomously—reducing leaks and avoiding expensive excavation work—have been developed by a team of engineers led by the University of Sheffield.

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