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
60CeO2-40TiO2 thin films were deposited on soda-lime silicate glass substrates by R.F. magnetron sputtering. The effects of heat-treatment on the UV-absorption of the thin films were studied on the 60CeO2-40TiO2 thin ...60CeO2-40TiO2 thin films were deposited on soda-lime silicate glass substrates by R.F. magnetron sputtering. The effects of heat-treatment on the UV-absorption of the thin films were studied on the 60CeO2-40TiO2 thin film with the largest UV cut-off wavelength. The sample films with CeO2：TiO2=60：40 were heated at 773 K, 873 K, 973 K for 30 min. These films are characterized by X-ray diffraction （XRD）, scanning electron microscopy （SEM）, and X-ray photoelectron spectroscopy and spectrometer （XPS）. XRD analysis proves that the addition of TiO2 to CeO2 changed the crystalline state of CeO2. But the UV absorption effect of CeO2-TiO2 films with CeO2 crystallite phase is inferior to that of the amorphous phase CeO2-TiO2 films. XPS analysis also indicates that the amorphous phase CeO2-TiO2 films have the most Ce3＋ content in these films. Amorphous phase and crystalline phase of the CeO2-TiO2 films have different effects on UV absorption of the thin films.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
As infectious respiratory diseases are highly transmissible through the air,researchers have improved traditional total volume air distribution systems to reduce infection risk.Multi-vent module-based adaptive ventila...As infectious respiratory diseases are highly transmissible through the air,researchers have improved traditional total volume air distribution systems to reduce infection risk.Multi-vent module-based adaptive ventilation(MAV)is a novel ventilation type that facilitates the switching of inlets and outlets to suit different indoor scenarios without changing ductwork layout.However,little research has evaluated MAV module sizing and air velocity selection,both related to MAV system efficiency in removing contaminants and the corresponding level of protection for occupants in the ventilated room.Therefore,the module-source offset ratio(MSOR)is proposed,based on the MAV module size and its distance from an infected occupant,to inform selection of optimal MAV module parameters.Computational fluid dynamics simulations illustrated contaminant distribution in a two-person MAV equipped office.Discrete phase particles modelled respiratory contaminants from the infected occupant,and contaminant concentration distributions were compared under four MAV air distribution layouts,three air velocities,and three module sizes considered using the MsOR.Results indicate that lower air velocities favour rising contaminant levels,provided the ventilation rate is met.Optimal contaminant discharge can be achieved when the line of outlets is located directly above the infected occupant.Using this parameter to guide MAV system design,85.7% of contaminants may be rendered harmless to the human body within 120 s using the default air vent layout.A more appropriate supply air velocity and air vent layout increases this value to 91.4%.These results are expected to inform the deployment of MAV systems to reduce airborne infection risk.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the National Natural Science Foundation of China(No.51032005)the Fundamental Research Funds for the Central Universities(Wuhan University of Technology)+1 种基金the China Postdoctoral Science Foundation(No.2012M511285)the Fund for the Young Innovative Team(Hubei University of Education)(No.2012KQ05)
文摘60CeO2-40TiO2 thin films were deposited on soda-lime silicate glass substrates by R.F. magnetron sputtering. The effects of heat-treatment on the UV-absorption of the thin films were studied on the 60CeO2-40TiO2 thin film with the largest UV cut-off wavelength. The sample films with CeO2：TiO2=60：40 were heated at 773 K, 873 K, 973 K for 30 min. These films are characterized by X-ray diffraction （XRD）, scanning electron microscopy （SEM）, and X-ray photoelectron spectroscopy and spectrometer （XPS）. XRD analysis proves that the addition of TiO2 to CeO2 changed the crystalline state of CeO2. But the UV absorption effect of CeO2-TiO2 films with CeO2 crystallite phase is inferior to that of the amorphous phase CeO2-TiO2 films. XPS analysis also indicates that the amorphous phase CeO2-TiO2 films have the most Ce3＋ content in these films. Amorphous phase and crystalline phase of the CeO2-TiO2 films have different effects on UV absorption of the thin films.
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.
基金supported by the National Natural Science Foundation of China[No.52078009]the special fund of Beijing Key Laboratory of Indoor Air Quality Evaluation and Control[No.BZ0344KF20-05]the joint research project of the Wind Engineering Research Center,Tokyo Polytechnic University(MEXT(Japan)Promotion of Distinctive Joint ResearchCenter Program)[No.JPMXP0619217840,No.JURC20202007].
文摘As infectious respiratory diseases are highly transmissible through the air,researchers have improved traditional total volume air distribution systems to reduce infection risk.Multi-vent module-based adaptive ventilation(MAV)is a novel ventilation type that facilitates the switching of inlets and outlets to suit different indoor scenarios without changing ductwork layout.However,little research has evaluated MAV module sizing and air velocity selection,both related to MAV system efficiency in removing contaminants and the corresponding level of protection for occupants in the ventilated room.Therefore,the module-source offset ratio(MSOR)is proposed,based on the MAV module size and its distance from an infected occupant,to inform selection of optimal MAV module parameters.Computational fluid dynamics simulations illustrated contaminant distribution in a two-person MAV equipped office.Discrete phase particles modelled respiratory contaminants from the infected occupant,and contaminant concentration distributions were compared under four MAV air distribution layouts,three air velocities,and three module sizes considered using the MsOR.Results indicate that lower air velocities favour rising contaminant levels,provided the ventilation rate is met.Optimal contaminant discharge can be achieved when the line of outlets is located directly above the infected occupant.Using this parameter to guide MAV system design,85.7% of contaminants may be rendered harmless to the human body within 120 s using the default air vent layout.A more appropriate supply air velocity and air vent layout increases this value to 91.4%.These results are expected to inform the deployment of MAV systems to reduce airborne infection risk.
