A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Intelligent floor plan design of modular high-rise residential building based on graph-constrained generative adversarial networks
Abstract In the context of Modular High-Rise Residential Buildings (MHRBs), designing floor plans involves intricate complexities due to the need for adherence to numerous domain-specific design rules. To address this issue, our research introduces a novel framework based on a Graph-Constrained Generative Adversarial Network (GC-GAN) specialized for generating MHRB floor plans. This enhanced GC-GAN incorporates knowledge graphs that encapsulate domain-specific constraints and guidelines, thereby generating floor plans that exhibit realism, diversity, and conformity to established design principles. Additionally, the framework integrates a sophisticated image-to-vector conversion algorithm that enables seamless alignment with a predefined flat-design standardization library. A salient feature of this framework is the automated generation of Building Information Modeling (BIM) models, which rigorously conform to the modularity specifications essential for efficient modular construction. The efficacy and practical applicability of our approach have been validated through an exhaustive analysis covering fifteen cases across five diverse scenarios.
Highlights Distill modular high-rise residential buildings' floor plans design knowledge. Pioneered a graphical structure representation for design knowledge. Introduced a GC-GAN framework with graph constraints for floor plan designs. Proposed a matching algorithm with a standard design library of BIM models. Addressed the traditional GANs randomness to meet design and production standards.
Intelligent floor plan design of modular high-rise residential building based on graph-constrained generative adversarial networks
Abstract In the context of Modular High-Rise Residential Buildings (MHRBs), designing floor plans involves intricate complexities due to the need for adherence to numerous domain-specific design rules. To address this issue, our research introduces a novel framework based on a Graph-Constrained Generative Adversarial Network (GC-GAN) specialized for generating MHRB floor plans. This enhanced GC-GAN incorporates knowledge graphs that encapsulate domain-specific constraints and guidelines, thereby generating floor plans that exhibit realism, diversity, and conformity to established design principles. Additionally, the framework integrates a sophisticated image-to-vector conversion algorithm that enables seamless alignment with a predefined flat-design standardization library. A salient feature of this framework is the automated generation of Building Information Modeling (BIM) models, which rigorously conform to the modularity specifications essential for efficient modular construction. The efficacy and practical applicability of our approach have been validated through an exhaustive analysis covering fifteen cases across five diverse scenarios.
Highlights Distill modular high-rise residential buildings' floor plans design knowledge. Pioneered a graphical structure representation for design knowledge. Introduced a GC-GAN framework with graph constraints for floor plan designs. Proposed a matching algorithm with a standard design library of BIM models. Addressed the traditional GANs randomness to meet design and production standards.
Intelligent floor plan design of modular high-rise residential building based on graph-constrained generative adversarial networks
Liu, Jiepeng (author) / Qiu, Zijin (author) / Wang, Lufeng (author) / Liu, Pengkun (author) / Cheng, Guozhong (author) / Chen, Yan (author)
2023-12-30
Article (Journal)
Electronic Resource
English
| Taylor & Francis Verlag | 2024
Structural Plan Schema Generation Through Generative Adversarial Networks
| Springer Verlag | 2024
Structural Plan Schema Generation Through Generative Adversarial Networks
| Springer Verlag | 2024