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.
Data Driven Design: Populating & Visualizing Walls for GraphRAG
The work presented in this blog post is a study on how to with code and visual tools the creation and representation of the data this research group has been using for a main project in Research Studio. It explores the integration of data-driven design methods to support sustainable facade development and align with environmental … Read more
Intelligent Prototyping using Robot & Arduino
Intelligent Prototyping: robotics and micro-controllers
The work presented in this blog post is an approach combining Robotics and Microcontrollers as preparation for a main Research Studio project focused on sustainable facades and environmental guidelines. This represents our first steps in exploring these fields and their connections with Artificial Intelligence in Architecture and the Built Environment. In the Robotics domain, the … Read more
AI theory: Using NLP – Graph Rag for AI Suggestions in Facades, based on environmental guidelines
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
