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.

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Innovative Urban Planning in Singapore: The Urban Playground Project

In the rapidly urbanizing landscape of Singapore, the demand for innovative urban planning solutions is paramount to accommodating the growing population while preserving the city-state’s unique cultural and environmental heritage. This project proposes an exploration of a playful design methodology to devise novel urban block typologies tailored to Singapore’s specific needs and constraints. This research … Read more

Urban Wind Flow Modeling with PINNs

Overview PaperLink WIP – This study specifically explores Physics-Informed Neural Networks (PINNs) and Computational Fluid Dynamics (CFD) to analyze pedestrian wind comfort. It aims to integrate these neural network models into a web application using Three.js and other tools. By utilizing data from OpenWeatherMap, and OpenStreetMap, the project aims to support real-time simulations and visualizations … Read more

Climate Sensitive Facade Intervention for Urban Heat Island Mitigation

Research Questions: Can climate sensitive facade interventions reduce the intensity of the Urban Heat Island effect within urban blocks in arid climates?“ Research Goal: The goal is to propose simplified, climate-sensitive facade interventions in highly urbanized cities that reduce energy loads by mitigating Urban Heat Island (UHI) effects, while complying with climate-resilient regulations. These interventions … Read more

Vehicle Crash Prediction in New York City

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

Hotel Prediction for Singapore

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

Barcelona Program

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

Rental Price Predictor – Amsterdam

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

Predictive Coastal Erosion

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

migrAItion

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

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

FacAid + Chatbot

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

Materializer

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

Project Sentinel: Predicative analysis of street lighting and safety

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

Kyoto Blossoms & Tourism Machine Learning

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

Predict Yelp Ratings based on Urban Data using GML

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

Temperature prediction in Argentina

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

100-Year Flood Risk Road Intersection Classification

Flood Risk Classification

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

Aquamines

Inhabiting Ice Cavities under the Martian Surface Introduction – The Idea For the first settlers on Mars, we have envisioned a colony of Aqua-Miners with the primary task of harvesting water as a resource from the ice beneath Mars’ surface, storing it and recycling it for sustainable use. Our approach to this problem has been … Read more

Genius Loci

View some of the collected data here: Street Types and Property Information Genius Loci was inspired by the work the of Sarah Williams in her book ‘Data action – Using data for public good’. In today’s data-driven world, the use of data has become increasingly pervasive, shaping various aspects of our lives from policymaking to … Read more

NOISENSE

NOISENSE emerges as a dynamic tool that facilitates the visualization of otherwise visually imperceptible phenomena within spaces. Through sound simulation and virtual prototyping techniques, NOISENSE aims to go beyond the limitations of human auditory perception, thereby enabling the visualization and replication of sound atmospheres. By conceptualizing sound as a spatial field, the online app allows … Read more

Wildlife Museum – an Architectural Cocoon

Spanning across an expansive natural sanctuary in Pozzuoli, Italy, the site for our study was equally challenging and interesting. The valley-like topography was crowned by a lake in the middle. The programme of imagining a wildlife museum at this site prompted us to think outside the box, to incur minimum impact on the site while … Read more

GRAPH MACHINE LEARNING TOWARDS ADAPTIVE CITIES: A data-driven approach to sea level rise problematics

Abstract This thesis explores experimental approaches when addressing sea level rise challenges , the impacts of land loss due to sea rise and population growth. It delves into considerations associated with innovative approaches while emphasizing the integration of urban syntax and graph machine learning. Drawing upon case studies and open data, underscoring the importance of … Read more

Superblock BCN

A Multidimensional Study of the Superilla My research starts from observing human behaviour. Often when we face a change in our lives, especially when it wasn’t planned, we feel resistance and it takes time before we accept and embrace it.  As you probably know Barcelona in the last few years lived some changes in its … Read more