A Study on the Improvement Plan of Classification System for Beam and Slab of Fire Resistance Construction in Building&amp;apos;s Law

An, Jae-Hong; Cho, Gyuhwan; Yeo, In-Hwan

doi:10.9798/kosham.2020.20.1.203

Cited by 3 publications

(2 citation statements)

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“…In the 3D model, more nonmathematical data like cost boundaries, market data, and cost change variables can be added. This implies that the development interaction or project plan can measure up according to the perspective of designing expenses, lessening configuration changes, and making the task speculation more precise and sensible [39][40][41][42][43].…”

Section: Simulation Outcomementioning

confidence: 99%

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Wang

2022

Wireless Communications and Mobile Computing

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The developing intricacy and extent of building projects, as well as the way that construction schedule management is still mostly done by hand, has resulted in low efficiency in construction schedule management, resulting in cost overruns and legal disputes as a result of schedule delays in many projects. Existing 3D reconstruction algorithms frequently result in large there may be holes, distortions, or hazy regions in remade 3D models, but AI-based 3D remaking strategies regularly reestablish straightforward isolated parts and portray them as 3D boxes. Accordingly, these algorithmic systems are generally not sufficient for certifiable use. The primary objective of this paper is to apply the creation ill-disposed network strategy to 3D recreation works on the nature of the underlying 3D recreation model via preparing a generative ill-disposed network model to a joined state. As solo examples, just recently noticed 2D pictures are required, with no dependence on earlier information on the 3D hidden shape or reference insights. Test results show that this algorithmic structure outflanks present status of-the-workmanship 3D reproduction approaches on an average 3D recreation test set. On the common 3D remaking test set, exploratory outcomes uncover that this algorithmic structure beats the current cutting edge 3D reproduction techniques.

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Section: Simulation Outcomementioning

confidence: 99%

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Wang

2022

Wireless Communications and Mobile Computing

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“…erefore, this paper adopts ResNet-101 network [37] as the main structure of discrimination network. Typical ResNet networks adopt back normalization, which makes the whole training process more stable.…”

Section: Network Structure Of Ss-gan-3dmentioning

confidence: 99%

Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Zhou

2021

Scientific Programming

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The increasing complexity and enormity of construction projects, as well as the fact that the actual operation of construction schedule management still mainly relies on traditional manual management methods, have led to low efficiency of construction schedule management and caused many construction projects to have cost overruns and legal disputes due to schedule delays. Existing 3D reconstruction algorithms often lead to significant voids, distortions, or blurred parts in the reconstructed 3D models, while the machine learning-based 3D reconstruction algorithms are often only to reconstruct simple separated objects and represent them as 3D boxes. A novel architecture of semisupervised 3D reconstruction algorithm is proposed. The algorithm iteratively improves the quality of the original 3D reconstruction model by training a generative adversarial network model to a converged state. Only the prior observed 2D images are required as weakly supervised samples, without any dependence on prior knowledge of the 3D structure shape or reference observations. Experimental results show that this algorithmic framework has significant advantages over the current state-of-the-art 3D reconstruction methods on the standard 3D reconstruction test set.

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Evaluation of Fire Resistance Performance and Temperature Distribution by Depth of Reinforced Concrete Slab Subjected to Fire

Yoon¹,

Choi²

2021

J. Korean Soc. Hazard Mitig

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The yield or ultimate strength of a structure can be deteriorated by fire, leading to structural collapse. Temperature estimation has been studied in concrete members to identify the internal temperature distribution of reinforced concrete members at high temperatures. While foreign standards such as ACI 216 and Eurocode exist, no domestic standards have not been established for fire-resistant designs (temperature of components, strength reduction factor). This study discussed the temperature estimation and comparison of slab members using the methods of Hertz, Wickstrőm, Kodur, Eurocode, and ACI 216.1 and analyzed the differences from actual experiments by comparing reinforced concrete experiments, foreign standards, and existing studies.

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A Study on the Improvement Plan of Classification System for Beam and Slab of Fire Resistance Construction in Building's Law

Cited by 3 publications

References 0 publications

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Evaluation of Fire Resistance Performance and Temperature Distribution by Depth of Reinforced Concrete Slab Subjected to Fire

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