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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.


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AI FOR ROBOTIC FABRICATION: Smarter Motion by Bridging Simulation and Reality in Robotic Clay 3D Printing

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MaAI-01 2024-2025

https://github.com/PaintDumpster/ai_for_robotic_fabrication.git In the field of robotic fabrication, precision is everything! Yet there’s often a significant gap between a simulated toolpath and the real-world movement of a robotic arm. Subtle discrepancies can lead to printing errors, structural issues, or failed prototypes. We explored how AI can be used to minimize this gap. This investigation sits at … Read more

Reinforcement Learning for Robotic Pick and Place

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MaAI 2024-2025

In pick-and-place robotics, a robotic arm must move from a start position to a target location (e.g., to pick or place an object) while safely navigating around obstacles. These obstacles may vary in size, severity, or risk — requiring the robot to adapt its path based on the workspace condition. This project simulates the core … Read more

Design Communication & Interfaces: Accesible and Interactive AI Suggestions in Facades, based on Environmental Guidelines

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MaAI-01 2024-2025

The work presented in this blog post explores how UX and UI tools can be used to create a more accesible and interactive platform to this research group’s main project in the Research Studio class. By using tools like Figma and programming in HTML, CSS and JAVA, we try to apply the learnings into AI … Read more

SeisNAV

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MaAI-02 2024-2025

Abstract: Natural disasters, especially earthquakes, often leave roads blocked, buildings collapsed, and maps outdated, making navigation and response efforts highly challenging, That’s where we operate. Our project SeisNAV is an AI-powered platform that combines satellite imagery and computer vision to detect collapsed structures and road blockages, providing real-time mapping and navigation tools for disaster response … Read more

Green Liminals: Feasibility Study for AI-Driven Budget Optimization for Urban Sustainable Solutions

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MaAI-01 2024-2025

Abstract The project explores innovative ways to address Barcelona’s sustainability challenges, such as carbon emissions, air pollution, and energy inefficiency. By leveraging AI, IoT sensors, and Nature-Based Solutions (NBS), the project aims to optimize municipal budgets for interventions that transform underutilized urban spaces into productive assets. The study highlights solutions like urban farming, greenery, and … Read more

Data Driven Design: Populating & Visualizing Walls for GraphRAG

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MaAI-01 2024-2025

The work presented in this blog post explores how code and visual tools can be used to create and represent the data central to this research group’s main project in the Research Studio. It focuses on integrating data-driven design methods to support sustainable façade development while aligning with environmental guidelines. By emphasizing the creation and … Read more

Intelligent Prototyping: Robotics and Micro-controllers

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MaAI-01 2024-2025

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

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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