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Master in AI for Architecture and the Built Environment 01

Within the current global context of rapid change, integrated with the potentials of digital technologies, IAAC’s Master in AI for Architecture and the built environement (MaAI01) is committed to the generation of new ideas and pioneer novel AI-driven solutions applications for the fields of Urban Tech, Smart Construction and Natural Ecosystems. The three transversal objectives of the program are Regenerative Design, Carbon Neutrality and Co-Design Processes.
In this context IAAC and the MaAI works with a multidisciplinary approach, integrating computational design, rapid prototyping, machine learning, data science, and generative AI implementing sustainable practices at an unprecedented scale. Facing the challenges posed by our environment and the future development of cities, architecture and buildings, through a virtuous combination of technology, biology, computational design, digital and robotic fabrication, pushing innovation beyond the boundaries of a more traditional architectural approach.

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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

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