This page has only limited features, please log in for full access.

Dr. Magdalena Grochulska-Salak
1. Department of Civil Engineering Warsaw University of Life Sciences-SGGW, 02-787 Warsaw, Poland

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Sustainable Development
0 Urban Agriculture
0 urban resilience
0 Blue and green infrastructure
0 Self-sufficient units

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 12 October 2020 in Energies
Reads 0
Downloads 0

The analyzed research issue provides a model for Carbon Footprint estimation at an early design stage. In the context of climate neutrality, it is important to introduce regenerative design practices in the architect’s design process, especially in early design phases when the possibility of modifying the design is usually high. The research method was based on separate consecutive research works–partial tasks: Developing regenerative design guidelines for simulation purposes and for parametric modeling; generating a training set and a testing set of building designs with calculated total Carbon Footprint; using the pre-generated set to train a Machine Learning Model;applying the Machine Learning Model to predict optimal building features; prototyping an application for a quick estimation of the Total Carbon Footprint in the case of other projects in early design phases; updating the prototyped application with additional features; urban layout analysis; preparing a new approach based on Convolutional Neural Networks and training the new algorithm; and developing the final version of the application that can predict the Total Carbon Footprint of a building design based on basic building features and on the urban layout. The results of multi-criteria analyses showed relationships between the parameters of buildings and the possibility of introducing Carbon Footprint estimation and implementing building optimization at the initial design stage.

ACS Style

Mateusz Płoszaj-Mazurek; Elżbieta Ryńska; Magdalena Grochulska-Salak. Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design. Energies 2020, 13, 5289 .

AMA Style

Mateusz Płoszaj-Mazurek, Elżbieta Ryńska, Magdalena Grochulska-Salak. Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design. Energies. 2020; 13 (20):5289.

Chicago/Turabian Style

Mateusz Płoszaj-Mazurek; Elżbieta Ryńska; Magdalena Grochulska-Salak. 2020. "Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design." Energies 13, no. 20: 5289.