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
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
Project Aim In this project we aim to predict the type of vehicle crash that can be foreseen in the city of New York based on the traffic volume, using Graph Neural Networks (GNNs). To develop this machine learning model we use 3 different datasets. The model could hold potential if developed further, to be … Read more
Our project derives from our observation that in Singapore, most of hotels are located along the east and southeast areas near Changi Airport. There are very few hotels on the west side of Singapore. We also take into account on factors that tourists and visitors consider when making a reservation. These factors include public transportation, … Read more
Introduction Accurate prediction of rental prices poses a significant challenge in dynamic real estate markets such as Amsterdam. Our research project explores the use of graph-based machine learning to improve the accuracy of such predictions. This methodology could be of interest to various actors in the real estate sector, including brokers, investors, and urban planners. … Read more
Why? Coastal erosion is a dynamic and complex process influenced by both natural factors and human activities. Natural causes such as wave action, tidal patterns, weather events, and rising sea levels due to global warming significantly contribute to the gradual wearing away of coastlines. Additionally, human interventions like coastal construction, sand mining, and deforestation exacerbate … Read more
Studying migration is crucial for urban planners and architects to anticipate and accommodate the influx of people into cities, ensuring the development of robust infrastructure that can support this growth. As migration patterns shape demographic changes, understanding these trends allows cities to plan for adequate housing, transportation, healthcare, and educational facilities. This foresight is essential … Read more
Who Why: For the Amenities, we selected the following due to its overall correlation with residential real estate and we anticipate that it will give a good indication of how the properties would be priced. By creating bounding boxes around the amenities, with a radius of 1km, we ensure that as much of the graph … Read more
Dataset Our database contains more than 181,000 rows, each with comprehensive information. The primary database includes 17 variables, though not all are necessary for our analysis. The most crucial data points are location (latitude and longitude), type of food establishment, type of inspection, inspection results, and risk level. As the person who uploaded the database … Read more
