An interactive assessment framework for residential space layouts using pix2pix predictive model at the early-stage building design

An interactive assessment framework for residential space layouts using pix2pix predictive model at the early-stage building design

Mostafavi, F. (TU Delft History, Form & Aesthetics)
Tahsildoost, Mohammad (Shahid Beheshti University)
Zomorodian, Zahra Sadat (Shahid Beheshti University)
Shahrestani, Seyed Shayan (Shahid Beheshti University)

2022

Purpose: In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design. Design/methodology/approach: A methodology using an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout. The proposed methodology is then evaluated by being applied to 300 sample apartment units in Tehran, Iran. Four pix2pix models were trained to predict illuminance, spatial daylight autonomy (sDA), primary energy intensity and ventilation maps. The simulation results were considered ground truth. Findings: The results showed an average structural similarity index measure (SSIM) of 0.86 and 0.81 for the predicted illuminance and sDA maps, respectively, and an average score of 88% for the predicted primary energy intensity and ventilation representative maps, each of which is outputted within three seconds. Originality/value: The proposed framework in this study helps upskilling the design professionals involved with the architecture, engineering and construction (AEC) industry through engaging artificial intelligence in human–computer interactions. The specific novelties of this research are: first, evaluating indoor environmental metrics (daylight and ventilation) alongside the energy performance of space layouts using pix2pix model, second, widening the assessment scope to a group of spaces forming an apartment layout at five different floors and third, incorporating the impact of building context on the intended objectives.

Artificial intelligence
Computer vision
Deep learning
Human–computer interactions
Pix2pix model
Space layout

http://resolver.tudelft.nl/uuid:edf45f33-f3b1-477b-886a-fae94d7f32ba

https://doi.org/10.1108/SASBE-07-2022-0152

2023-07-01

2046-6099

Smart and Sustainable Built Environment, 1-19

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 F. Mostafavi, Mohammad Tahsildoost, Zahra Sadat Zomorodian, Seyed Shayan Shahrestani