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- Title
一种高层建筑群排布生成与推荐 算法.
- Authors
郭茂祖; 曹印庚; 王鹏跃; 赵玲玲; 李 阳
- Abstract
With consideration for the norms and constraints of high- rise building layouts regarding fireproof building design and residential district planning, an automatic layout method for high-rise residential buildings based on reinforcement learning and unsupervised learning was proposed. It provides references for automatic generation of building layouts and pattern clustering of building clusters for architects and designers. Firstly, building cluster layout was designed by using a deep deterministic policy gradient algorithm based on reinforcement learning. A reward function with considerations for design norms of land use, fire separation distance, and sunshine duration was designed. Specifically, the bonus value of land use redline was used as the overlapping area between areas enclosed by four vertexes of the current building and the involved land area. The bonus value of fire separation distance was designed as outward rounded rectangles by centering the involved building to make distances from each point to the building monomers agree with specific distance scope. This was used as the fire prevention region. The overlapping area between the fire prevention region of the building under planning and the fire prevention region of other buildings was calculated. A bonus value of sunshine duration was designed and calculated from each frame. Meanwhile, the number of sunshine test points which fail to meet the sunshine duration as well as the calculation of sunshine duration at the sunshine test points of each building were provided. This model inputs the initial layout given by architects and each building monomer was moved according to the given order. The building was fixed after moving for several steps, thus completing the layout of all buildings in order. Finally, the layout generated by the model was judged. If the design scheme meets all constraints, it is a reasonable scheme. Several layout schemes that meet the presetting constraints were output through continuous interaction, training, and learning between building monomers and environments. Next, the generated reasonable layouts were expressed in modes. The buildings were extracted as points. Centroids of different individual buildings were chosen to form the pattern shape of building cluster. Pattern expressions of the generated reasonable layouts were realized using Fourier descriptors. With consideration for the rotational invariance of Fourier descriptors, the directional characteristics of building cluster were added. The minimum external rectangle expressed in each layout pattern was chosen and the included angle between the long-axis of the rectangle and positive direction of x-axis were used as the directional characteristics of building clusters. The seven-dimensional characteristics were normalized and the generated reasonable layouts were clustered using a K-means clustering algorithm. Different clusters with similar building patterns were formed and a multi- element typical design mode after elimination of redundancy was given, which provided references to architects. Finally, the feasibility and effectiveness of the proposed method were verified in a real environment in a block in Beijing. A total of 87 groups of schemes were output over 80,000 rounds. Moreover, the generated schemes were divided into three types to be used as design references by architects. Compared with traditional automatic design processes, the proposed method doesn't need a layout of samples and can provide several layout schemes meeting the preset constraints for a block.
- Subjects
BEIJING (China); BUILDING reinforcement; BUILDING layout; REINFORCEMENT learning; CONSTRUCTION planning; FIRE prevention; SKYSCRAPERS; FUZZY algorithms
- Publication
South Architecture / Nanfang Jianzhu, 2022, Issue 9, p96
- ISSN
1000-0232
- Publication type
Article
- DOI
10.3969/j.issn.1000-0232.2022.09.012