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.
Project Aim The idea behind the tool is straightforward, that is to hand-sketch louver lines over a facade image to demonstrate the potential reduction in solar radiation on the facade which is linked to a domino effect on the chain of sustainability, as reduction in radiation on the facade will lead to reduction of energy … Read more
Skin Sense is a project that aims to help users optimize the thermal comfort of their interior spaces by applying skin shaders. Why implement this? What can skin sense help the user with? Once the designer designs the skin for the building facade it has to be analyzed for indoor comfort. To analyze indoor comfort … Read more
Introduction Joo Chiat’s shophouses in Singapore are emblematic of the country’s rich Peranakan heritage, blending architectural elements from Chinese, Malay, and European traditions. Established in the 1920s and 1930s, these colorful and intricately decorated buildings are located in a conservation area that underscores both cultural preservation and modern adaptation. Notable for their historical and aesthetic … Read more
In Switzerland, biking to work is highly encouraged and widely practiced. This is facilitated by the country’s extensive network of bike lanes, bike-friendly infrastructure, and commitment to sustainability However, in terms of safety, Switzerland saw 5,287 cyclist fatalities in 2022, up from 3,793 in 2020. Over the past five years, cycling injuries have increased by … Read more
1. Introduction 2. Methodology 2a. Dataset Generation To train our machine learning model effectively, we needed a substantial dataset of at least 1.000 data points. However, conducting CFD simulations is notoriously time-consuming, presenting a significant challenge in generating such a large volume of data efficiently. To streamline the dataset generation process, we broke down the … Read more
Bike route classification using Graph Machine Learning The goal of the project was to develop a graph machine learning model that would predict existence of bike routes in Singapore based on collected geodata. Because the bike routes were represented by graph edges, that was also our classification type. Data Exploration & Research Topic We choose … Read more
Block Classification This project aims to classify urban blocks in Barcelona, focusing on the districts of Saint Martí and Eixample, based on their dominant functions. By identifying the primary uses of these urban blocks, we can gain insights into the spatial organization and functional distribution within the research area. Saint Martí and Eixample, two vibrant … Read more
Predicting median income through Traffic Volume, Green and Paved Surface Analysis For our research, we selected Phoenix, Arizona due to its significant disparity in the distribution of green spaces. As depicted in the image, areas with higher income levels display dense green coverage, whereas lower income areas are predominantly paved. The image above depicts the … Read more
DF Predictor aims to revolutionize daylight prediction in architectural design by implementing the conditional generative adversarial network (cGAN) Pix2Pix to predict daylight factors, motivated by the need to improve efficiency and accuracy in daylight analysis. Daylight factor analysis, an integral part of the early design stages and mandated by building codes, is typically lengthy due … Read more
The 30-Year Plan for Greater Adelaide(South Australia), initiated in 2010, aimed to shift away from urban sprawl by fostering a more condensed, pedestrian-friendly urban landscape. The plan emphasised revitalising current neighbourhoods, focusing development along transit routes, and introducing mixed-use areas to connect jobs, services, and public transit with residential zones. Recognising the unsustainable nature of … Read more
A Self-Reflection on AI’s Replication and Innovation in Architecture Our group explores the topic of AI-chitect, a self reflection on AI’s replication and innovation in architecture. The proposal is to initialise Vitruvius’ De Architectura using RAG (Retrieval-Augmented Generation) and set the volume of text as a knowledge base from which our journey as the AI-chitect … 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
We have 2 target approaches : In the first approach we Test some of the relevant human Anthropometric measurements with a chair parameters and predict the Ergo Class. The second approach is to input the Human Anthropometric measurements with the desired Ergo Class and predict a range of chair parameters matching this Ergo Class. For … Read more
In an era where Artificial Intelligence (AI) is rapidly evolving, understanding its potential and risks is crucial. To explore these dimensions, we staged a fictitious debate featuring three contrasting AI agents. This debate delves into the ethical implications, societal benefits, and possible downsides of an advancing AI era. Project Overview This project staged a fictitious … Read more
This project is the final submission for our Data Encoding course, where we learned the fundamentals of Machine Learning. For this project, we were required to use only numerical features, meaning all training data for our Machine Learning model had to be in numerical form. This involved encoding architectural and spatial features into numbers. “Daylight … Read more
Concept In a world where urban areas are predominantly developed and the heat island effect is intensifying, the construction industry significantly contributes to environmental challenges. Instead of focusing on tools that promote new construction, our goal is to provide a tool that analyzes existing buildings and suggests improvements. This approach aims to enhance sustainability and … 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
The LegoLizer Vision The core idea behind LegoLizer is to train a machine learning model that can predict and evaluate the use of specific modules to achieve a certain geometry.In this exercise, we use LEGO modules to train and predict on our features. This project leverages the power of computational design and machine learning to … Read more
Abstract 3D-SOLIDS Component Classification tool utilizes machine learning to optimize the placement of essential 3D components such as walls, doors, windows, floors, and railings in residential floor plans. By analyzing spatial features and architectural attributes, it automates and enhances the design process, ensuring accuracy, consistency, and compliance with building standards. This tool aids architects and … Read more
This project aimed to develop a daylight predictor to facilitate and generate well-informed adaptive reuse projects, with a specific focus on providing sustainable design solutions for low-income housing. Los Angeles (LA) was selected as a case study due to its proactive open data initiatives and commitment to adaptive reuse. This proposal provides a snapshot of … Read more
Adaptive reuse is gaining traction as a key strategy for urban regeneration and sustainability. This approach repurposes existing buildings, conserving resources and preserving cultural heritage. However, challenges such as economic viability and technical difficulties often complicate these projects. This is particularly evident in urban centers such as Los Angeles, where the re-purposing of heritage buildings … Read more
Our goal was to predict the relationship between the tourist activity zones and the airbnb rentals. Tourism is vital to Spain’s economic growth, with Barcelona as a key contributor, accounting for over 12% of the country’s GDP. TOURIST ANALYSIS AND DATA Tourist spots and zones can be broadly classified to tourist amenities – transits, bus … Read more
