Go Back

Master in AI for Architecture and the Built Environment

IAAC’s Master in AI for Architecture & the Built Environment is a unique program oriented towards leading the change in decarbonising human activities and crafting a more sustainable, resilient future urbanisation for our planet. Through an innovative curriculum deeply rooted in AI applications, the program pioneers novel AI-driven solutions that not only respond to the pressing challenges of our time but also set a new standard for environmentally and socially conscious co-design and planning. The Master in AI for Architecture & the Built Environment is training the professionals that city administrations, governments, industries, and communities need, to transform the built environment in the era of digital technologies.


Filtered (3)
Filters
Years
Term
Course
Tag
Show (3)
Cancel

Green Enough? Assessing Barcelona’s Compliance with EEA Green Space Standards Per Capita Using Geospatial Analysis.

by
MaAI 2024-2025

AbstractGreen spaces play a critical role in enhancing urban livability by improving air quality, reducing urban heat island effects, and promoting mental and physical well-being.This project evaluates the spatial distribution of green spaces in Barcelona and their compliance with the European Environment Agency (EEA) standard of 9 m² of green space per person. By overlaying … Read more

Evolutionary Algorithm to Optimize Resource Allocation for Sustainable Solutions  

by
MaAI 2024-2025

The problem we are addressing is the critical challenge of resource allocation for sustainable solutions in cities. Our focus is on how to meet environmental standards effectively by choosing efficient solutions while  staying within budget.The challenge cities face is resource allocation. Urban sustainability demands meeting environmental standards within tight and specific budgets. The task is … Read more

AI theory: Using NLP – Graph Rag for AI Suggestions in Facades, based on environmental guidelines

by
MaAI-01 2024-2025

This project leverages Graph Retrieval-Augmented Generation (Graph RAG) to provide intelligent facades configuration recommendations aligned with the New European Bauhaus (NEB) principles. This post presents how we apply AI theory approaches for reaching the stablished target. The project correlates quantitative data and qualitative guidelines, by integrating 17% of the assessed metrics in this guidelines and creating … Read more