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 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
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 mitigate … Read more
Introduction Our project, Materializer, leverages the power of multiple self-trained machine learning models to predict material quantities based on an image uploaded by the user and the building coordinates. This innovative approach utilizes image segmentation to isolate buildings, image classification to read material pixels, and a height prediction model for buildings lacking height information in … Read more
Greater Manchester, one of the largest and most vibrant urban centers in the UK, is characterized by its substantial student population and a dynamic economic landscape. With approximately 120,670 university students during the 2021/22 academic year and a significant number of these individuals studying at the University of Manchester and Manchester Metropolitan University, the region … Read more
Project Focus For our Graph Machine Learning project, wanted to look at the city of Kyoto and Tourism Behavior around Cherry blossom patterns. Specifically, we set out to find out how cherry blossom patterns might affect the behavior and walking paths of tourists within the city. Data Sets We started by collecting our data sets … Read more
Hypothesis The goal of this research was to investigate if open spatial data could predict Yelp ratings utilizing graph machine learning (GML) methods. We hypothesize that urban phenomena, events, and objects will indicate customer reviews and popularity, and therefore, could be used predict ratings. In particular, we perform edge classification using the DGL library. For … Read more
Insights into the effects of weather data in Argentina, with implications for sustainable land management, deforestation and conservation policies, agriculture, industry and economies. Climate change, in particular the decrease in precipitation, is predicted to have significant effects on the living conditions in Argentina, affecting agricultural production, sea level rise, hydroelectric energy. The dataset composition consists … Read more
Beginnings Our project’s concept is to develop a classification model that identifies street intersections (graph nodes) in Stockholm, Sweden, susceptible to flooding during a 100-year flood event. Based on our initial research, we found that graph machine learning operates at three levels: the graph, its edges, and its nodes. With access to a 100-year flood … Read more
There are different types of loneliness and different causes for each type. The social media induced loneliness as described by Sherry Turkle in her book Alone Together: Why We Expect More from Technology and Less from Each Other (2012) is different from the loneliness of feeling alone in a crowd in a very large city … Read more
Our project focuses on modeling and predicting habitat fragmentation avoidance mechanisms using graphs. We investigate wildlife corridors in Australia, leveraging graph-based approaches to create coherent passages and connect forest areas. By analyzing satellite images, road data, and other metrics, we construct and evaluate graph models that incorporate forest containment, node characteristics, and connectivity. Our goal … Read more
Meanders on the Lower Mississippi River In the battle between architecture and nature, nature tends to win. Therefore, it is important that we understand how rivers move over time so that we can know the best and the worst places to build. Our project studies the Lower Mississippi River from Cairo, Illinois to New Orleans, … Read more
Problem statement and project objectives How can we create a routing system that orients onto its users environment, utilizing open source data? How do we collect and process data? Our objective is to utilize the existing data and generate any missing data required for each aspect of our analysis. We combine data from different sources … Read more
