2017
DOI: 10.14257/ajmahs.2017.06.90
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3D Architecture Modeling and Quantity Estimation using SketchUp

Abstract: The construction cost is estimated based on the drawings at the design stage and constructor will find efficient construction methods for budgeting and budgeting appropriate to the budget. Accurate quantity estimation and budgeting are critical to determining whether the project is profitable or not. However, since this process is mostly performed depending on manpower or 2D drawings, errors are likely to occur and The BIM(Build Information Modeling) program, which can be automated, is very expensive and diffi… Show more

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Cited by 2 publications
(2 citation statements)
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“…The results of this research align with the theory that SketchUp is an application consisting of an intuitive 3D mode that allows users to create and edit 2D and 3D models using the patented "push and pull" technique. Through these features, designer can transform any flat surface into a 3D shape (Asanbekova, 2020;Kim et al, 2017). This is further supported by research indicating that SketchUp is known for its simplicity and accessibility (Brightman, 2018;Wang et al, 2012).…”
Section: ) Productionmentioning
confidence: 74%
“…The results of this research align with the theory that SketchUp is an application consisting of an intuitive 3D mode that allows users to create and edit 2D and 3D models using the patented "push and pull" technique. Through these features, designer can transform any flat surface into a 3D shape (Asanbekova, 2020;Kim et al, 2017). This is further supported by research indicating that SketchUp is known for its simplicity and accessibility (Brightman, 2018;Wang et al, 2012).…”
Section: ) Productionmentioning
confidence: 74%
“…Feiyan et al [16] proposed to use DL-based architecture and longterm and short-term memory to model the spatiotemporal correlation of mobile traffic distribution, respectively. Kim and Um [17] designed an edge computing system for health monitoring and treatment and extracted features from mobile sensor data by CNN (convolutional neural network), which played an important role in their epileptiform localization application. Zaman et al [18] proposed a deep edge learning framework and proved its superiority in reducing network traffic and running time.…”
Section: Related Workmentioning
confidence: 99%