Machine learning techniques for diagrid building design: Architectural–Structural correlations with feature selection and data augmentation-Reference-Cited by-同舟云学术

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Machine learning techniques for diagrid building design: Architectural–Structural correlations with feature selection and data augmentation

Published:2024-06 Issue: Volume:86 Page:108766
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Kazemi Pooyan^ORCID,Entezami Alireza^ORCID,Ghisi Aldo^ORCID

Publisher

Elsevier BV

Reference73 articles.

1. Intelligent computing: the latest advances, challenges, and future;Zhu;Intelligent Computing,2023

2. Reducing embodied carbon in structural systems: A review of early-stage design strategies;Fang;J. Build. Eng.,2023

3. Parametric framework for early evaluation of prescriptive fire design and structural feasibility in tall timber;Hens;J. Archit. Eng.,2023

4. Linking structure and parametric geometry;Preisinger;Archit. Des.,2013

5. Machine learning applications for building structural design and performance assessment: State-of-the-art review;Sun;J. Build. Eng.,2021

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-objective optimization of a bistable curved shell with controllable thickness based on machine learning;Theoretical and Applied Mechanics Letters;2024-05

2. Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids;Buildings;2024-04-16

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