Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Automated Building Layout Generation Through Deep Resnet Architecture
Conventional methods of planning the layout of a building often involve labor-intensive manual operations that result in solutions that are less than ideal. There is a possibility that the automation of building plan generation could be a game-changer in this business, and the development of deep learning algorithms has opened the door to this possibility. The incorporation of ResN et architecture into the study will assist in overcoming these limitations, which will result in a greater understanding of spatial linkages as well as the capacity to design architectural layouts that are more realistic and varied. The proposed method involves training a deep ResN et model with a dataset consisting of several building layouts. This model is able to capture the intricate interactions that occur between the various architectural components. It is possible to ensure adaptation and flexibility to a variety of design requirements by using the trained model to generate new building plans in accordance with user-defined criteria. In addition to demonstrating that the proposed method is effective, the results also demonstrate how the deep ResN et architecture can deliver a wide range of realistic building layouts.
Automated Building Layout Generation Through Deep Resnet Architecture
Conventional methods of planning the layout of a building often involve labor-intensive manual operations that result in solutions that are less than ideal. There is a possibility that the automation of building plan generation could be a game-changer in this business, and the development of deep learning algorithms has opened the door to this possibility. The incorporation of ResN et architecture into the study will assist in overcoming these limitations, which will result in a greater understanding of spatial linkages as well as the capacity to design architectural layouts that are more realistic and varied. The proposed method involves training a deep ResN et model with a dataset consisting of several building layouts. This model is able to capture the intricate interactions that occur between the various architectural components. It is possible to ensure adaptation and flexibility to a variety of design requirements by using the trained model to generate new building plans in accordance with user-defined criteria. In addition to demonstrating that the proposed method is effective, the results also demonstrate how the deep ResN et architecture can deliver a wide range of realistic building layouts.
Automated Building Layout Generation Through Deep Resnet Architecture
Sykam, Mohan Vamsi (Autor:in) / Kumar, G. Viswanatha (Autor:in) / P, Senthil kumar (Autor:in) / V, Varun (Autor:in)
15.03.2024
377604 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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