Graph-based approach for enumerating floorplans based on users specifications

Shekhawat, Krishnendra; Jain, Rahil; Bisht, Sumit; Kondaveeti, Aishwarya; Goswami, Dipam

doi:10.1017/s0890060421000275

Cited by 2 publications

(2 citation statements)

References 34 publications

(54 reference statements)

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“…This platform allows users to obtain an automated floor plan by entering basic parameters such as room functions, areas, and connections. Some researchers regard a floorplan as a polygon and use graph theory for floorplan automation (Shekhawat et al, 2021). Some researchers focus on the hierarchies of graph and different layout generation algorithms based on them (Koenig and Knecht, 2014).…”

Section: Floorplan and Graphmentioning

confidence: 99%

Predicting Non-functional Nodes of Floorplan via Graph Neural Network (GNN)

Liu,

Li,

Koehler

et al. 2023

eCAADe Proceedings

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This paper presents an innovative approach to automating the floorplan generation process by employing Graph Neural Networks (GNN) to facilitate the transition from functional assignment lists to bubble diagrams and eventually to floorplan graphs. In recent years, there have been many studies on the interconversion of floorplan graph and layout design. However, these studies usually mix up floorplan graph and bubble diagram, despite their distinct roles in representing spatial and functional relationships, respectively. To address this disparity, we introduce a research framework comprising three main steps. First, we generate the CubiBubble5k dataset, which encompasses bubble diagrams and functional lists, drawing on the existing CubiCasa5k and CubiGraph5k datasets. Next, we train a GNN to transform design assignments into structured graph data, utilizing functional lists as input and bubble diagrams as output. Subsequently, we train another GNN that predicts and inserts non-functional spaces, such as corridors and anterooms, into purely functional bubble diagrams, using bubble graphs as input and floorplan graphs as output. We assess the performance of both GNNs and, by integrating our framework with the established graph2plan study, successfully demonstrate the generation of real-world floorplans from project task books. Lastly, we conduct case studies to validate the feasibility of our proposed framework. We use the existing graph2plan platform to visualize the impact of our algorithm on the final layout.

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Section: Floorplan and Graphmentioning

confidence: 99%

Predicting Non-functional Nodes of Floorplan via Graph Neural Network (GNN)

Liu,

Li,

Koehler

et al. 2023

eCAADe Proceedings

View full text Add to dashboard Cite

show abstract

“…Any tools developed to support decision making should also consider the human characteristic. Approaches such as graph-based representations and causal ordering for parsimonious models produced can facilitate the model's transparency and enable efficient human decision-making by being aligned with the human cognition (Goel et al , 2012; Shekhawat et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

Graph models for engineering design: Model encoding, and fidelity evaluation based on dataset and other sources of knowledge

Coatanéa

Nagarajan

Mokhtarian

et al. 2023

AIEDAM

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Automatically extracting knowledge from small datasets with a valid causal ordering is a challenge for current state-of-the-art methods in machine learning. Extracting other type of knowledge is important but challenging for multiple engineering fields where data are scarce and difficult to collect. This research aims to address this problem by presenting a machine learning-based modeling framework leveraging the knowledge available in fundamental units of the variables recorded from data samples, to develop parsimonious, explainable, and graph-based simulation models during the early design stages. The developed approach is exemplified using an engineering design case study of a spherical body moving in a fluid. For the system of interest, two types of intricated models are generated by (1) using an automated selection of variables from datasets and (2) combining the automated extraction with supplementary knowledge about functions and dimensional homogeneity associated with the variables of the system. The effect of design, data, model, and simulation specifications on model fidelity are investigated. The study discusses the interrelationships between fidelity levels, variables, functions, and the available knowledge. The research contributes to the development of a fidelity measurement theory by presenting the premises of a standardized, modeling approach for transforming data into measurable level of fidelities for the produced models. This research shows that structured model building with a focus on model fidelity can support early design reasoning and decision making using for example the dimensional analysis conceptual modeling (DACM) framework.

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Graph-based approach for enumerating floorplans based on users specifications

Cited by 2 publications

References 34 publications

Predicting Non-functional Nodes of Floorplan via Graph Neural Network (GNN)

Predicting Non-functional Nodes of Floorplan via Graph Neural Network (GNN)

Graph models for engineering design: Model encoding, and fidelity evaluation based on dataset and other sources of knowledge

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