Improving model-based structural engineering with application usage data
Pynnönen, Sannamari (2023)
2023
Rakennustekniikan DI-ohjelma - Master's Programme in Civil Engineering
Rakennetun ympäristön tiedekunta - Faculty of Built Environment
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Hyväksymispäivämäärä
2023-10-31
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202310218961
https://urn.fi/URN:NBN:fi:tuni-202310218961
Tiivistelmä
Over the past decade, the utilisation of building information modelling (BIM) has increased. Building information models are precise 3D representations of building structures with geometric data vital for construction and design. From these models it is possible to extract different kinds of data such as, material, geometry information, location data and relational attributes.
This thesis explores the analysis of existing data collected during building modelling and detailing to enhance structural modelling and make it more efficient. The primary objective is to identify data patterns, such as user groups, use-case scenarios, and potential challenges, to identify areas of improvement.
The research methods used are literature review and case study to gather knowledge about the data collection and analysis process. The case study puts this knowledge into action and analyses the usage data that is collected. Key findings from the analysis highlight the importance of simplifying complex components. Additionally, tailoring user interfaces to accommodate specific user groups and workflows enhance user experience and make modelling more efficient.
Future research directions include investigating what improvements to the Tekla and component user interfaces could be implemented. In addition, there is potential in implementation of a component recommendation system using artificial intelligence with automated component settings.
This thesis explores the analysis of existing data collected during building modelling and detailing to enhance structural modelling and make it more efficient. The primary objective is to identify data patterns, such as user groups, use-case scenarios, and potential challenges, to identify areas of improvement.
The research methods used are literature review and case study to gather knowledge about the data collection and analysis process. The case study puts this knowledge into action and analyses the usage data that is collected. Key findings from the analysis highlight the importance of simplifying complex components. Additionally, tailoring user interfaces to accommodate specific user groups and workflows enhance user experience and make modelling more efficient.
Future research directions include investigating what improvements to the Tekla and component user interfaces could be implemented. In addition, there is potential in implementation of a component recommendation system using artificial intelligence with automated component settings.