Page 2: Research news on AI in built environment

AI in the built environment encompasses data-driven and physics-informed methods to model, predict, and optimize the performance and resilience of buildings and infrastructure. Machine learning and advanced simulation are applied to building energy use, HVAC control, and solar and wind power forecasting, as well as to hazard-related phenomena such as subsidence, floods, fire, and seismic events. The field also integrates retrofitting strategies, climate-smart housing design, and regulatory considerations to enhance energy efficiency, comfort, and disaster resilience in urban systems.

Energy & Green Tech

New tool identifies raw materials used in buildings

Circular-economy thinking is becoming increasingly important in the construction sector. It helps reduce raw material consumption and emissions and can provide a well-thought-out reuse and recycling scheme to cut costs.

Energy & Green Tech

Digital energy scheduler cuts marginal carbon-reduction costs

A digital scheduling framework combining machine learning, optimization and blockchain technology could reduce carbon emissions at a lower cost in regional energy systems, according to research in the International Journal ...

Energy & Green Tech

AI optimizes energy production and cuts carbon emissions in seconds

A new AI model for predicting energy consumption could help managers plan ahead and open up opportunities for cutting carbon emissions, according to research published in the International Journal of Information and Communication ...

Engineering

Engineers work toward a hurricane-resilient Texas

Torrential downpours. Storm surge floods. Thunder and lightning. High winds of more than 100 mph (161 km/h). Hurricanes are among the most devastating natural hazards to affect Texans. Damage causes many disruptions, including ...

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