Machine Learning Algorithm-Based Tool and Digital Framework for Substituting Daylight Simulations in Early-Stage Architectural Design Evaluation

Radziszewski, Kacper; Waczynska, Marta

doi:10.22360/simaud.2018.simaud.001

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…This accounts for a difference of 600 times faster than the CFD engine when presented with similar data. However, this difference is to be expected, as similar results were observed in the work of Layout 5 19 and Kacper Radziszewski & Marta Waczyńska 20 in their daylight analysis. Nonetheless, this emphasizes the efficiency of machine learning models and supports the arguments made in this study to help designers obtain relatively accurate CFD airflow predictions in less time.…”

Section: Padded Data Set Resultssupporting

confidence: 79%

Generative Machine Learning Models for Airflow Prediction of Architectural Spaces

Rodriguez

2022

Resilient Futures

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Buildings have been identified as one of the biggest contributors of negative environmental impacts worldwide, more specifically energy usage due to the use of air conditioning and mechanical ventilation. Strategies such as cross-ventilation have become a reliable alternative to diminish some of these effects. However, designing for cross ventilation is no easy feat, as it requires architects and designers to study in detail the building context, overall massing design and building enclosure to maximize airflow potential. On the other hand, Computer Fluid Dynamics (CFD) airflow simulations are not used as often in architectural settings primarily due to time constraints, lack of performance metrics and quality assurance. The proper use of CFD airflow simulations involves a complex setup and run-time process, due to the large mathematical calculations involved.This study aims to apply existing generative machine learning algorithms to compute CFD wind velocity simulations to significantly shorter run times while maintaining a relatively high accuracy level, during the initial design stages. To test the proposed hypothesis, multiple machine learning models were created, trained, and tested. The evaluation metrics for these models consisted of using different image similarity methods to compare the images produced by the machine learning model to their CFD engine counterparts. The results obtained indicate that GAN application for CFD airflow predictions can produce acceptable results showing a significant run time difference of over a minute between the CFD simulation and the machine learning model. Having evaluated and proven this study as a proof of concept, this can set the precedents for further research on the use of CFD airflow simulations and machine learning within architectural practice. Allowing architects and designers to incorporate the use of CFD airflow simulations within their workflows.

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Section: Padded Data Set Resultssupporting

confidence: 79%

Generative Machine Learning Models for Airflow Prediction of Architectural Spaces

Rodriguez

2022

Resilient Futures

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A predictive model for daylight performance based on multimodal generative adversarial networks at the early design stage

Li,

Yuan,

Liu

et al. 2024

Energy and Buildings

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Machine Learning Model for Glare Prediction in Offices With Simple Architectural Features

Kurian

Colaco

et al. 2022

Journal of Green Building

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Daylight glare index (DGI), daylight glare probability (DGP) and glare-sensation (GS) predictive models are the widely used glare indices for the assessment of occupant visual comfort in daylit spaces. This paper presents the development and implementation of Machine Learning models to predict these glare indices. The training and validation data sets were collected from sensors incorporated in the test room with motorized Venetian Blinds and dimmable LED luminaires. Predictor and response data were obtained from conventional sensors, digital cameras, and the EVALGLARE Software. The regression models predict DGI and DGP, whereas the classification model predicts GS. In addition to standard statistical error evaluation metrics, the hypothesis test assesses the performance of regression/classification models. The results reveal that Ensemble Tree (ET) models are highly accurate at predicting glare indices. The proposed technique attempts to simplify the existing traditional Glare Index(GI) estimation method. The combination of real-time daylight glare prediction and suitable window shading control increases occupant visual comfort. A high dynamic image-based system is employed to verify the measurements made using traditional sensors.

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Machine Learning Algorithm-Based Tool and Digital Framework for Substituting Daylight Simulations in Early-Stage Architectural Design Evaluation

Cited by 3 publications

References 9 publications

Generative Machine Learning Models for Airflow Prediction of Architectural Spaces

Generative Machine Learning Models for Airflow Prediction of Architectural Spaces

A predictive model for daylight performance based on multimodal generative adversarial networks at the early design stage

Machine Learning Model for Glare Prediction in Offices With Simple Architectural Features

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