Building Information Modeling Connection Recommendation Based on Machine Learning Using Multimodal Information

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2023-08-21
Department
Major/Subject
Master's Programme in Security and Cloud Computing (SECCLO)
Mcode
SCI3113
Degree programme
Master’s Programme in Security and Cloud Computing (SECCLO)
Language
en
Pages
71
Series
Abstract
The increasing complexity of construction projects gives rise to the need for an efficient way of designing, managing, and maintaining structures. Building Information Modeling (BIM) facilitates these processes by providing a digital representation of physical structures. Tekla Structures (TS) has emerged as a popular building information modeling software for structural design. In structural engineering, connections play an important role in joining various building objects. However, the efficient and accurate design of connections in TS remains a challenge due to the wide range of available connection types. Existing solutions for connection recommendation often rely on predefined rules, limiting their applicability and requiring time-consuming setup. Recent research has explored machine learning approaches for connection recommendation, but they suffer from scalability issues or high computational costs. This thesis addresses the connection type recommendation problem in TS as a classification task, leveraging the diverse representations of the BIM objects, including 2D images and attributes. This thesis improves existing approaches for single modality data, comparing XGBoost with random forest for attributes, while enhancing the previous CNN model for image classification. Furthermore, this thesis investigates the potential of combining images and attribute data for connection type classification, using two multimodal data fusion strategies: late fusion and intermediate fusion. The results show that XGBoost with metadata of the attribute dataset yields the best performance, with a maximum accuracy of 0.9283, and the experimented multimodal data fusion methods are unable to further optimise the classification results. The accuracy of attribute-based methods is improved by up to 0.6 percent. The improvement in CNN model can enhance the classification accuracy by up to 5 percent. By comparing various data sources and approaches, this thesis aims to provide a practical connection recommendation design, thereby laying a foundation for better connection design processes in construction projects.
Description
Supervisor
Jung, Alexander
Thesis advisor
Schmidt, Fabian
Filoche, Pascal
Keywords
building information modeling,, TeklasStructures, connection, classification, machine learning, multimodal data fusion
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