The Metaverse 2.0 for an environmentally conscious design If environmental consequences could be quickly computed by AI and reflected in an immersive digital model environment, the AEC industry could change the way it delivers results and reduce its carbon footprint? The research proposal is to investigate the “Infoverse”, an enhanced Metaverse for designers, where they … Read more
The construction industry creates a waste that is expected to reach 2.2 billion tons globally by 2025 (Transparency market research). Because of this the market started trying different new techniques to construct with less waste, that is how with the 4.0 industrial revolution came the Additive manufacturing process, that fabricates physical 3D objects layer by … Read more
Welcome Everyone My Name is Anton Klyshnia, I am a student of MaCad program at the Institute of Advanced Architecture of Catalonia. You are listening to the podcast as a part of Course: BIM and Smart Construction Theory. Today we will discuss how the AI will influence the architectural software and BIM software in particular. … Read more
Goal: to predict trip duration of NYC cabs using machine learning models. Tools: Python + Nympy + Pandas + Datetime + Plotly.express + Matplotlib + Math + Seaborn + Bokeh + Sklearn Stages of project: data cleaning, data analysis, data preparation, data testing, evaluating prediction accuracy. Data cleaning The first dataset visualization with splitting datasets … Read more
Ciudades urbana y humanamente digeribles Quetzaltenango, Guatemala. C.A Anteriormente el tema se aborda desde la perspectiva de ecología y cambio climático, donde se expone la situación actual de la ciudad de Quetzaltenango y donde se identifican los procesos alimentarios a traves de la industria y como estas dinámicas crean ciudades enfermas y obviamente ciudadanos enfermos, … Read more
Machine Learning-based Analysis of NYC Cab Trip Duration “Data-Driven Rides” is an entry for the first MaCT Machine Learning Competition that hosted on Kaggle which involves predicting the duration of taxi rides in New York City. The dataset provided for this competition is based on the 2016 NYC Yellow Cab trip record dataset and the … Read more
The MaCT01 students were tasked with training a model that would be used to predict NYC yellow taxi ride durations using machine learning. The dataset included pickup and drop-off datetimes, location coordinates and passenger count. Visualizing the data helped to understand the correlation between the columns and remove the highly correlated values Understanding the distribution … Read more
The source dataset provided for this project is derived from the 2016 NYC Yellow Cab trip records, which were made publicly available on the Big Query platform of the Google Cloud. The data was originally collected and published by the New York City Taxi and Limousine Commission (TLC). This dataset serves as the foundation for … Read more
#Objective #A Kaggle Competition to MaCT01 students to show their knowledge, designing an end-to-end machine learning project to predict the “Trip Duration” of NYC Taxi trips. #Workflow #First of all a workflow hast do be developed, which represents a classic approach for training machine learning models, analysing the provided training data provided by the submission, … Read more
The aim of the project is to predict trip duration, using 2016 NYC YELLOW CAB TRIP DATA. Structuring the dataset The analysis begins with outlier identification. The passenger_count variable has two outliers: 0 and 9, which compared to the amount of people allowed by the NY Limousine law, is impossible. Also, there were some pickup … Read more
INTRODUCTION Urban planning decisions have a significant impact on the development of cities, and using machine learning can provide decision-makers with valuable insights to make informed decisions. By clustering urban areas based on various factors such as population density, built density, POI density, green cover, and build diversity, we can reveal spatial patterns that can … Read more
The project reflects on how people in ancient times defined natural and weather phenomena. The proposal is to create an open public space – Inspired by the concept of an ancient sanctuary with places that used to be built for worship and predictions of weather conditions, the Meteo Sanctum consists of digital indicators that allow … Read more
During this Hardware seminar, we were able to understand different types of sensors used in robotics and the techniques used to process the collected data. The aim of the seminar is to use the sensors to understand the environment and then expect the robot to take decisions (automation) and carry a specific task (actuation). For … Read more
THERMAL QUESTIONS: Q1. Having studied the sun path diagram, it is observed that there are low sun angles in the east and the west throughout the year. How can thermal heat gain be prevented from the east and the west direction? – To prevent the heat gain from the lower angle of the sun from … Read more
Objective The objective of the project is to create a database of stickers located in the district of Gotico, Barcelona by collecting geolocated photos, analyzing & sorting them by machine learning and further research to then create a framework for evaluating and improving local economy, artists and political movements marketing strategies. Strategy By collecting geolocated … Read more
“A Data-Driven Approach to Balancing Creative Class Development and Social Equity in Barcelona” The creative industries have been acknowledged as one of the most important sectors for economic growth and development in current cities worldwide. This sector represents a space of culture where all artistic and creative expression that pro-actively enters into dialogue with new … Read more
