Updating surrogate models in early building design via tabular transfer learning DOI
Laura Hinkle,

Nathan C. Brown

Building and Environment, Год журнала: 2024, Номер 267, С. 112307 - 112307

Опубликована: Ноя. 12, 2024

Язык: Английский

Could AI Ethical Anxiety, Perceived Ethical Risks and Ethical Awareness About AI Influence University Students’ Use of Generative AI Products? An Ethical Perspective DOI
Wenjuan Zhu, Lei Huang,

Xinni Zhou

и другие.

International Journal of Human-Computer Interaction, Год журнала: 2024, Номер unknown, С. 1 - 23

Опубликована: Март 8, 2024

The study aims to explore the factors that influence university students' behavioral intention (BI) and use behavior (UB) of generative AI products from an ethical perspective. Referring decision-making theory, research model extends UTAUT2 with three influencing factors: awareness (EA), perceived risks (PER), anxiety (AIEA). A sample 226 students was analysed using Partial Least Squares Structural Equation Modelling technique (PLS-SEM). results further validate effectiveness UTAUT2. Furthermore, performance expectancy, hedonistic motivation, price value, social all positively BI products, except for effort expectancy. Facilitating conditions habit show no significant impact on BI, but they can determine UB. extended perspective play roles as well. AIEA PER are not key determinants BI. However, directly inhibit From mediation analysis, although do have a direct UB, it inhibits UB indirectly through AIEA. Ethical Nevertheless, also increase PER. These findings help better accept ethically products.

Язык: Английский

Процитировано

23
Developing surrogate models for the early-stage design of residential blocks using graph neural networks DOI Creative Commons
Zhaoji Wu, Mingkai Li, Wenli Liu

и другие.

Building Simulation, Год журнала: 2025, Номер unknown

Опубликована: Янв. 22, 2025

Язык: Английский

Процитировано

3
Fusing Transformer and Diffusion for High-Resolution Prediction of Daylight Illuminance and Glare based on Sparse Ceiling-Mounted Input DOI

Yujian Huang,

Tiancheng Zeng,

Meilin Jia

и другие.

Building and Environment, Год журнала: 2024, Номер unknown, С. 112163 - 112163

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

6
A Comparative Analysis of Polynomial Regression and Artificial Neural Networks for Prediction of Lighting Consumption DOI Creative Commons
Pavol Belány, Peter Hrabovský, Štefan Šedivý

и другие.

Buildings, Год журнала: 2024, Номер 14(6), С. 1712 - 1712

Опубликована: Июнь 7, 2024

This article presents a comparative analysis of two prominent machine learning techniques for predicting electricity consumption in workplace lighting systems: polynomial regression and artificial neural networks. The primary objective is to assess their suitability applicability developing an accurate predictive model. After brief overview the current state energy-saving techniques, examines several established models energy buildings systems. These include networks, support vector machines. It then focuses on practical comparison between network-based looks at data preparation process, outlining how used within each model establish appropriate prediction functions. Finally, it describes methods evaluate accuracy developed functions allow based external intensity. evaluates using root mean square error, correlation coefficient determination values. compares these values obtained both models, allowing conclusive assessment which provides superior

Язык: Английский

Процитировано

5
Generative Design in the Built Environment DOI Creative Commons

Zhi Xian Chew,

Jing Ying Wong, Yu Hoe Tang

и другие.

Automation in Construction, Год журнала: 2024, Номер 166, С. 105638 - 105638

Опубликована: Июль 27, 2024

Язык: Английский

Процитировано

5
Daylight Factor prediction using Machine Learning: a two-way study using numerical encoding and regression models, versus image encoding and pix2pix DOI
Alejandro Pacheco Diéguez,

L. Pacheco,

Hande Karataş

и другие.

Building and Environment, Год журнала: 2025, Номер unknown, С. 112743 - 112743

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
A Deep Convolutional Generative Adversarial Network (DCGAN) for the fast estimation of pollutant dispersion fields in indoor environments DOI Creative Commons
Claudio Alanis Ruiz, M.G.L.C. Loomans, T. van Hooff

и другие.

Building and Environment, Год журнала: 2025, Номер unknown, С. 112856 - 112856

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Enhancing Performance and Generalization in Dormitory Optimization Using Deep Reinforcement Learning with Embedded Surrogate Model DOI

Zhifei Shi,

Chen‐Yu Huang, Jinyu Wang

и другие.

Building and Environment, Год журнала: 2025, Номер unknown, С. 112864 - 112864

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
A simple yet powerful dimensionality reduction method for annual daylighting prediction and its inverse process via pix2pix DOI Creative Commons
Daoru Wang,

Wayne Place,

S. Li

и другие.

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112410 - 112410

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Prediction and Optimization of Daylight Performance of AI-generated Residential Floor Plans DOI

Xiao Jie Hu,

Hao Zheng,

Dayi Lai

и другие.

Building and Environment, Год журнала: 2025, Номер unknown, С. 113054 - 113054

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0