Investigation on visualisation of architectural design and civil engineering structural design based on artificial intelligence

This article integrates 3D visualisation and AI technologies, specifically the YOLO object detection model, into architectural and structural design to enhance project quality and efficiency. Using the BIM visualisation platform, it presents a data-driven, standardised, and shared design information model. The platform extracts 3D structural features from spatial data, offering functions like data sharing, information exchange, and linkage processing. Tests show that the predicted construction cycle matches the actual value, with a cycle ratio < 0.9 and shear weight ratio > 1.60%, meeting specifications. YOLO demonstrates the highest accuracy and lowest error margin among tested algorithms. It aids in predicting on-site construction progress, optimising structural solutions, and enhancing intelligent visualisation effects for architectural and structural design.

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Publication Details

Journal
International Journal of Materials and Product Technology
Published
2025-01-01
DOI
https://doi.org/10.1504/ijmpt.2025.147082
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
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article

Investigation on visualisation of architectural design and civil engineering structural design based on artificial intelligence

Liang Xuan, Hongming Pan
International Journal of Materials and Product Technology
BIM and Construction Integration
article

Investigation on visualisation of architectural design and civil engineering structural design based on artificial intelligence

Liang Xuan, Hongming Pan
article en

Abstract

This article integrates 3D visualisation and AI technologies, specifically the YOLO object detection model, into architectural and structural design to enhance project quality and efficiency. Using the BIM visualisation platform, it presents a data-driven, standardised, and shared design information model. The platform extracts 3D structural features from spatial data, offering functions like data sharing, information exchange, and linkage processing. Tests show that the predicted construction cycle matches the actual value, with a cycle ratio < 0.9 and shear weight ratio > 1.60%, meeting specifications. YOLO demonstrates the highest accuracy and lowest error margin among tested algorithms. It aids in predicting on-site construction progress, optimising structural solutions, and enhancing intelligent visualisation effects for architectural and structural design.

International Journal of Materials and Product TechnologyVol. 70(1)
Chongqing Technology and Business University (CN), Hunan University (CN), Loudi Central Hospital (CN)
Sustainable cities and communities
Openalex Percentile: Top 81%
BIM and Construction Integration
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