Carbon nanotube-reinforced cement composites have gained significant attention due to their enhanced mechanical properties,particularly in compressive and flexural strength.Despite extensive research,the influence of ...Carbon nanotube-reinforced cement composites have gained significant attention due to their enhanced mechanical properties,particularly in compressive and flexural strength.Despite extensive research,the influence of various parameters on these properties remains inadequately understood,primarily due to the complex interactions within the composites.This study addresses this gap by employingmachine learning techniques to conduct a sensitivity analysis on the compressive and flexural strength of carbon nanotube-reinforced cement composites.It systematically evaluates nine data-preprocessing techniques and benchmarks eleven machine-learning algorithms to reveal tradeoffs between predictive accuracy and computational complexity,which has not previously been explored in carbon nanotube-reinforced cement composite research.In this regard,four main factors are considered in the sensitivity analysis,which are the machine learning model type,the data pre-processing technique,and the effect of the concrete constituent materials on the compressive and flexural strength both globally through feature importance assessment and locally through partial dependence analysis.Accordingly,this research optimizes ninety-nine models representing combinations of eleven machine learning algorithms and nine data preprocessing techniques to accurately predict the mechanical properties of carbon nanotube-reinforced cement composites.Moreover,the study aims to unravel the relationships between different parameters and their impact on the composite’s strength by utilizing feature importance and partial dependence analyses.This research is crucial as it provides a comprehensive understanding of the factors influencing the performance of carbon nanotube-reinforced cement composites,which is vital for their efficient design and application in construction.The use of machine learning in this context not only enhances predictive accuracy but also offers insights that are often challenging to obtain through traditional experimental methods.The findings contribute to the field by highlighting the potential of advanced data-driven approaches in optimizing and understanding advanced composite materials,paving the way for more durable and resilient construction materials.展开更多
This paper provides an overview of South Korea’s 20-year journey in adopting building information modeling(BIM) and future direction. It first discusses the six phases of BIM adoption in South Korea, starting from th...This paper provides an overview of South Korea’s 20-year journey in adopting building information modeling(BIM) and future direction. It first discusses the six phases of BIM adoption in South Korea, starting from the use of BIM as a marketing tool to its current intelligent BIM phase. The government’s support for BIM-related research and development projects is also highlighted, with a focus on the artificail intelligence (AI)-based architectural design automation project. As the future direction, it explores the integration of AI with BIM in both local and global contexts. The paper presents AIpowered architectural design methods, including AI-powered early architectural design generation and architectural detailing.Compared to AI-based early architectural design generation, architectural detailing is an unexplored research topic. This paper introduces two AI-and BIM-based architectural detailing methods, being developed at Yonsei University:namely,BIM library transplant and Natural language-based Architectural Detailing through Interaction with AI (NADIA). These methods demonstrate how AI-enhanced BIM can enable architects to interactively develop building details using a language model as a conversational AI and a knowledge base, and a BIM authoring tool as a design platform, in the near future.展开更多
文摘Carbon nanotube-reinforced cement composites have gained significant attention due to their enhanced mechanical properties,particularly in compressive and flexural strength.Despite extensive research,the influence of various parameters on these properties remains inadequately understood,primarily due to the complex interactions within the composites.This study addresses this gap by employingmachine learning techniques to conduct a sensitivity analysis on the compressive and flexural strength of carbon nanotube-reinforced cement composites.It systematically evaluates nine data-preprocessing techniques and benchmarks eleven machine-learning algorithms to reveal tradeoffs between predictive accuracy and computational complexity,which has not previously been explored in carbon nanotube-reinforced cement composite research.In this regard,four main factors are considered in the sensitivity analysis,which are the machine learning model type,the data pre-processing technique,and the effect of the concrete constituent materials on the compressive and flexural strength both globally through feature importance assessment and locally through partial dependence analysis.Accordingly,this research optimizes ninety-nine models representing combinations of eleven machine learning algorithms and nine data preprocessing techniques to accurately predict the mechanical properties of carbon nanotube-reinforced cement composites.Moreover,the study aims to unravel the relationships between different parameters and their impact on the composite’s strength by utilizing feature importance and partial dependence analyses.This research is crucial as it provides a comprehensive understanding of the factors influencing the performance of carbon nanotube-reinforced cement composites,which is vital for their efficient design and application in construction.The use of machine learning in this context not only enhances predictive accuracy but also offers insights that are often challenging to obtain through traditional experimental methods.The findings contribute to the field by highlighting the potential of advanced data-driven approaches in optimizing and understanding advanced composite materials,paving the way for more durable and resilient construction materials.
基金funded by the Civil Engineering Graphics Branch of China Graphicsthe Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land,Infrastructure and Transport (Grant RS-2021-KA163269)。
文摘This paper provides an overview of South Korea’s 20-year journey in adopting building information modeling(BIM) and future direction. It first discusses the six phases of BIM adoption in South Korea, starting from the use of BIM as a marketing tool to its current intelligent BIM phase. The government’s support for BIM-related research and development projects is also highlighted, with a focus on the artificail intelligence (AI)-based architectural design automation project. As the future direction, it explores the integration of AI with BIM in both local and global contexts. The paper presents AIpowered architectural design methods, including AI-powered early architectural design generation and architectural detailing.Compared to AI-based early architectural design generation, architectural detailing is an unexplored research topic. This paper introduces two AI-and BIM-based architectural detailing methods, being developed at Yonsei University:namely,BIM library transplant and Natural language-based Architectural Detailing through Interaction with AI (NADIA). These methods demonstrate how AI-enhanced BIM can enable architects to interactively develop building details using a language model as a conversational AI and a knowledge base, and a BIM authoring tool as a design platform, in the near future.
