City-Oriented Modeling for Observational Radiative Thermal comfort CONCEPT The idea behind C.O.M.F.O.R.T is to provide an effective tool for architects and urban planners to design cooler and more sustainable cities, leveraging the power of machine learning and AI technologies. FACTORS OF IMPACT To create COMFORT we had to study the different factors of impact which … Read more
Daylight autonomy is a climate-based metric that measures the percentage of occupied hours during which a given space receives a specific amount of light, typically 300 lux, through natural daylight alone. It is used to evaluate energy efficiency in building designs by evaluating how much artificial lighting can be reduced throughout the year (Lorenz et … Read more
Abstract Architectural drawings and construction documentation are critical in the construction industry, serving as the main means of communication between different disciplines and acts as a blueprint on guiding the construction of the built environment. However, errors in these drawings are quite common and often leads to significant delays, cost overruns, and structural issues. This … Read more
Vocal Commands for Autodesk Revit Problem Despite decades of technological advancement, the core user experience between man and computer has remained nearly unchanged. This tedious and repetitive process requires designers to spend extreme amounts of time that could be saved through updated methods. This thesis explores a potential future for computer user experience, utilizing AI … Read more
Thermal Insight would be a standalone app that optimizes indoor environments by predicting thermal comfort. With simulations from trained models to help enhance occupant comfort and energy efficiency, evaluating metrics like PMV, PPD, and MRT. This tool would help improve well-being and productivity while achieving efficiency goals. Abstract Methodology Use Case Data Analysis Results and … 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
We embarked on a fascinating journey through the latest advancements in Image Synthesis and Language Models within the Architecture, Engineering, and Construction (AEC) industry. The course highlighted the transformative potential of generative AI, enabling architects, engineers, and designers to push the boundaries of traditional design processes, streamline workflows, and tackle complex challenges in groundbreaking ways. … Read more
The whole AEC industry is challenged by the high CO2 footprint caused by the increasing population and construction. Introducing regulations to the construction industry is a solution to reduce CO2 footprint. The workflow behind Leed mini consultant goes as follows: starting with gathering the regulation in a dataset and setting it up, training a rag … 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
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
Project goal For our graph Machine Learning project we chose to work with the city of Copenhagen. Our goal was to predict the Road Noise in the city. Dataset and data preparation From the KOBENHAVNS OPEN DATASET, we obtained a dataset detailing road noise levels across Copenhagen. To fill in the gaps where data was … 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
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
