A Study on Guesthouse Interior Space Planning Method Based on BIM Topological Relationships

Building Information Modeling (BIM) technology has revolutionized the architectural and construction industries by providing detailed digital representations of buildings, enhancing design efficiency and project management. Despite its widespread application, the utilization of BIM in optimizing interior space planning, particularly in boutique accommodations like guesthouses, remains underexplored. This research addresses the gap by introducing a novel method for interior environment space planning of guesthouses based on the topological relationships derived from BIM data. This study developed a new algorithm that utilizes detailed topology information from BIM in order to make space planning decisions more efficiently, thereby improving space utilization efficiency and guest experience. Through a comprehensive analysis of BIM topological relationships and the application of advanced optimization techniques, our method aims to optimize the use of space while considering the unique constraints and requirements of guesthouse environments. The proposed algorithm demonstrates significant improvements in spatial efficiency and design quality when tested against traditional planning methods on real-world BIM datasets. This research not only contributes a novel approach to the field of architectural design and planning but also offers practical implications for the enhancement of interior spaces in guesthouses, potentially influencing future applications of BIM technology in the hospitality industry.

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Publication Details

Journal
International Journal of High Speed Electronics and Systems
Published
2024-10-28
DOI
https://doi.org/10.1142/s0129156425400555
Primary Topic
Korean Urban and Social Studies
Type
article
Field-Weighted Citation Impact
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article

A Study on Guesthouse Interior Space Planning Method Based on BIM Topological Relationships

Kun Yao
International Journal of High Speed Electronics and Systems
Korean Urban and Social Studies
article

A Study on Guesthouse Interior Space Planning Method Based on BIM Topological Relationships

Kun Yao
article en

Abstract

Building Information Modeling (BIM) technology has revolutionized the architectural and construction industries by providing detailed digital representations of buildings, enhancing design efficiency and project management. Despite its widespread application, the utilization of BIM in optimizing interior space planning, particularly in boutique accommodations like guesthouses, remains underexplored. This research addresses the gap by introducing a novel method for interior environment space planning of guesthouses based on the topological relationships derived from BIM data. This study developed a new algorithm that utilizes detailed topology information from BIM in order to make space planning decisions more efficiently, thereby improving space utilization efficiency and guest experience. Through a comprehensive analysis of BIM topological relationships and the application of advanced optimization techniques, our method aims to optimize the use of space while considering the unique constraints and requirements of guesthouse environments. The proposed algorithm demonstrates significant improvements in spatial efficiency and design quality when tested against traditional planning methods on real-world BIM datasets. This research not only contributes a novel approach to the field of architectural design and planning but also offers practical implications for the enhancement of interior spaces in guesthouses, potentially influencing future applications of BIM technology in the hospitality industry.

International Journal of High Speed Electronics and SystemsVol. 34(01)
Aba Teachers University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 72%
Korean Urban and Social Studies
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A Study on Guesthouse Interior Space Planning Method Based on BIM Topological Relationships — Kun Yao · International Journal of High Speed Electronics and Systems (2024) | TGRS Research Map | TGRS