BIM product recommendation for intelligent design using style learning

Zhou, Xiaoping; Cheng-xi, MA; Wang, Mengmeng; Guo, Maozu; Guo, Zhi; Xia, Liang; Han, Junjun

doi:10.1016/j.jobe.2023.106701

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Three retrieval approaches, such as ZS-SBIR [45], DrStyle [30], and the proposed one, are chosen to compare the BIM product retrieval, as shown in Figure 5. In Figure 5, the ZS-SBIR approach retrieves the models based mainly on the shape contents.…”

Section: Bim Retrieval Resultsmentioning

confidence: 99%

“…For the proposed work, because of the long-range style dependencies, the bar chairs with "comfortable" and "formal" styles can be retrieved, and the tables with "simply robust" and "natural beauty" styles can be recommended, regardless of color differences and slightly different spatial gestures. Three retrieval approaches, such as ZS-SBIR [45], DrStyle [30], and the proposed on are chosen to compare the BIM product retrieval, as shown in Figure 5. In Figure 5, t ZS-SBIR approach retrieves the models based mainly on the shape contents.…”

Section: Bim Retrieval Resultsmentioning

confidence: 99%

“…For BIM product recommendation, similarity computation is costly in 3D space. As a result, Zhou et al [30] proposed the DrStyle model to use style learning algorithms on 2D image snapshots with the most style richness information. Inspired by this work, the stylistic features extracted from snapshots of 3D models are used to classify the different styles and make stylistic recommendations.…”

Section: Research Frameworkmentioning

confidence: 99%

“…In three-dimensional space, various spatial gestures and positions lead to quite different 2D snapshots. DrStyle [30] introduces the image evaluation module to select the one with rich stylistic features. Different from DrStyle, it is found that slight spatial differences will not cause different style saliencies at the patch level, as shown in Figure 2.…”

Section: Long-range Style Dependenciesmentioning

confidence: 99%

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BIM Product Style Classification and Retrieval Based on Long-Range Style Dependencies

Cui,

Zang,

Liu

et al. 2023

Buildings

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The rapid increase in building components on the building information model (BIM) object database has created new demand for BIM product recommendations to improve design efficiency. Current efforts mainly focus on the shape and contents of the products, instead of stylistic consistency, which is a crucial factor during the practical design process. To tackle such a problem, this paper proposes a novel framework to capture stylistic features based on long-range design dependencies with structural preservation, of which the snapshots of BIM products have been used to extract the stylistic features; core patches with strong style, generated by the pre-trained saliency model, are the root nodes; stylistic correlations are calculated as the hyperedges by tree-based operations; deep features and design features are proposed to represent the low-level and style distribution based on the study of design theory; and an ensemble learning strategy is introduced to solve the unbalanced classifier performance. An ablation study is conducted to validate the effectiveness of the proposed framework, in which comparative experiments with state-of-the-art baselines demonstrate the advantages of the proposed method.

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Section: Bim Retrieval Resultsmentioning

confidence: 99%

Section: Bim Retrieval Resultsmentioning

confidence: 99%

Section: Research Frameworkmentioning

confidence: 99%

Section: Long-range Style Dependenciesmentioning

confidence: 99%

See 2 more Smart Citations

BIM Product Style Classification and Retrieval Based on Long-Range Style Dependencies

Cui,

Zang,

Liu

et al. 2023

Buildings

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Digital Design of Intelligent Plant Based on Reverse Engineering

Zhang,

Wang,

Chen

et al. 2024

Lecture Notes in Electrical Engineering

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Automatic lift path planning of prefabricated building components using semantic BIM, improved A* and GA

Chen,

You,

Zhang

et al. 2024

ECAM

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PurposePrefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).Design/methodology/approachFirstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.FindingsThe proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.Originality/valueIn this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

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BIM product recommendation for intelligent design using style learning

Cited by 6 publications

References 28 publications

BIM Product Style Classification and Retrieval Based on Long-Range Style Dependencies

BIM Product Style Classification and Retrieval Based on Long-Range Style Dependencies

Digital Design of Intelligent Plant Based on Reverse Engineering

Automatic lift path planning of prefabricated building components using semantic BIM, improved A* and GA

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