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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-Driven Inclusive Design: Topology Graph Analysis and NLP for Accessibility Solutions in Architecture

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MaAI 2024-2025

This project focuses on improving the accessibility and inclusivity of hospital environments for visually impaired individuals. Centered on Spain’s Primary Care Centres (CAPs), the initiative addresses critical navigation challenges faced by this community. By leveraging AI-driven tools, including topological graph analysis and semantic segmentation, the project provides architects with actionable insights to design healthcare spaces … Read more

Implementation of CNNs, SOMs and SVMs in post disaster analysis with drones

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MaAI-02 2024-2025

Introduction:The AI Theory class has provided us with comprehensive foundation in artificial intelligence, covering both fundamental principles and advanced methods. Through topics such as clustering, neural networks, evolutionary computing, and decision-making models, the course aims to equip students with the theoretical knowledge and practical insights needed to apply AI across various fields, including disaster management … Read more

Evolutionary Algorithm to Optimize Resource Allocation for Sustainable Solutions  

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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

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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