The MaCAD is a unique online programme training a new generation of architects, engineers and designers ready to develop skills into the latest softwares, computational tools, BIM technologies and AI towards innovation for the Architecture, Engineering and Construction (AEC) industry.
Our cities have long been shaped around cars — wide roads, narrow sidewalks, and green spaces replaced by parking lots. Cars have quietly become the measure of how we design our streets. But what if cities were designed for people instead? As Fred Polak wrote in Images of the Future, the act of visualizing possible … Read more
Introduction Noise is one of the most pervasive environmental stressors in cities. Long-term exposure has been linked to sleep disturbance, cardiovascular disease, reduced cognitive performance, and lower overall quality of life. Yet despite its importance, detailed noise maps are surprisingly difficult to obtain. Producing official noise maps requires measurements, traffic models, and considerable technical effort. … Read more
ML-Driven Plant Placement for Adaptive Architecture BioSpatial Intelligence explores how machine learning can support planting decisions in architectural spaces. The project starts from a simple design question: when we design a building, how can we decide which plants belong to which environmental conditions? Instead of relying only on intuition, we developed a workflow that reads … Read more
Objective Traditional flood risk maps take months to produce, are updated only every few years, and are too coarse, they might say a whole district is at risk without telling you which specific street or field will actually be underwater. “We are going to predict whether any given location in Thessaly, Greece is Flood-Prone or … Read more
Can we predict what kind of use does a city grid hosts — Commercial vs. Residential — from its built form, morphology, and proximity to other urban features? “Can we predict the land use of a space based on existing environmental information from official and unofficial sources?” What’s the sweet spot for a Machine Learning … Read more
In the Cloud-Based Data Management seminar, our final project explored how parametric design tools can move beyond the desktop and become accessible through the web. The course focused on transforming Grasshopper definitions into web-based applications using Rhino.Compute, Vue, and web visualization workflows, with the goal of making computational design more accessible to end users. This … Read more
Cities are warming rapidly, and dense urban centers feel it the most — especially areas with little shade or vegetation. Heat doesn’t affect everyone equally: groups like the elderly, youth, and people with health conditions are far more vulnerable. Problem Statement Most existing tools focus on UTCI, the Universal Thermal Climate Index. It measures how … Read more
Introduction Hyperbuilding 03 In our project, the building is conceived not as an isolated object, but as a vertical living tissue within the broader human organism of the “Vertical Machine”. Much like a network of functional cells, it sustains life, maintains environmental balance, and coordinates with the system’s “lungs” and “heart.” This biological metaphor guided … Read more
Abstract This research investigates how Building Information Modeling (BIM) can be leveraged to generate structured and meaningful architectural environments for architectural visualization, video games, and animated films. It explores workflows between BIM tools and real-time game engines, focusing on the transfer and preservation of spatial logic and metadata. The study aims to demonstrate how BIM-based … Read more
Introduction — Where My Interest Began My interest in AI and its application in the AEC industry started before this course. During the IAAC GSS, I worked on a study in Bogotá using GIS and image‑recognition tools to map sensory criticality in care-focused urban zones. What surprised me most was how accessible the process was. … Read more
Andromeda is a Rhino-native copilot that turns briefs, context, and massing into a single, interpretable graph so designers can enrich masterplans, evaluate KPIs, and bake results back to Rhino, without leaving their workflow. Built for early-stage masterplanning, it aims to raise social value, connectivity, accessibility, and overall urban quality by unifying data, guiding program allocation, … Read more
HaBiCoM is an AI-powered copilot designed to assist architects, designers, and users in shaping smarter, more responsive interior spaces. At its core, HaBiCoM redefines the way we approach furniture placement within residential environments by aligning each design decision with the user’s unique daily habits, lifestyle rhythms, and thermal comfort needs. Unlike traditional layout tools, HaBiCoM … Read more
Abstract In this project, we explore how graph-based thinking can reshape construction planning by bridging design data and scheduling logic. Drawing inspiration from modular architecture and network theory, we investigate new ways to visualize, analyze, and optimize the sequencing of building elements. By combining insights from BIM, parametric modeling, and graph analysis, the work aims … Read more
Thermal Comfort Index Prediction // Definition OBJECTIVE: predicting thermal comfort in air-conditioned residential buildings using machine learning algorithms. What is indoor thermal comfort? Thermal comfort is “That condition of mind that expresses satisfaction with the thermal environment” (ISO 7730) People may feel that their surroundings are warm, cold or simply comfortable depending on the thermal state … Read more
GWP-data enriched graphs Imagine being able to see the carbon footprint of a building, not after it’s built, but while it’s still a sketch. What if architects and engineers could get real-time sustainability feedback the moment they decide on a material or tweak a wall layout? That’s the vision behind Visual GWP. We combined Graph … Read more
