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.
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
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
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
