School of Arts and Media, Hefei Normal University, Hefei, Anhui 230601, China.
School of Science, Anhui Agricultural University, Hefei, Anhui 230036, China.
Comput Intell Neurosci. 2022 Jan 30;2022:7544113. doi: 10.1155/2022/7544113. eCollection 2022.
The present work aims to improve the design efficiency and optimize the results in the increasingly complex and diversified material design projects to help architects realize the better performance of building structures. According to the characteristics of comprehensive perception and intelligent processing of the Internet of Things, a reverse suspension structure design model is constructed based on the finite element method and simulated annealing algorithm. Besides, deep learning is adopted to train complex functions for performance correction and to optimize the plane structure of shell structure. Moreover, the force is transformed into shape, and the form-finding process is completed to facilitate the operation of designers. Finally, the spatial anchoring ability of the geographic information system is used to match and calculate the relevant characteristics of spatial elements. On this basis, the index construction strategy based on weight distribution is employed to realize the data fusion diagnosis framework and enhance the intelligence of architectural design. The simulation results show that the maximum tensile stress of the physical suspension experiment is 3.71 MPa and the maximum compressive stress is 14.7 MPa. The compressive stress value is much larger than the tensile stress value. The maximum deformation value's difference between the compressive and tensile stress is 0.07 and 0.11, respectively. The error is within the acceptable range, which is similar to the compression state results obtained from the actual suspension physical experiment, indicating that the initial design model of the reverse suspension structure based on deep learning is reliable. In addition, the evolutionary optimization effect analysis results demonstrate that the load of the design structure is relatively uniform, which verifies the feasibility of the algorithm reported here. The research significance of the reverse suspension structure model constructed here is to provide an accurate and feasible design idea for the reverse design of some complex structures in the building suspension. It can also shorten the creation and improvement cycle of this kind of structure and optimize the performance and construction cycle of the building structure.
本工作旨在提高设计效率并优化日益复杂和多样化的材料设计项目的结果，以帮助建筑师实现更好的建筑结构性能。根据物联网的综合感知和智能处理的特点，基于有限元方法和模拟退火算法构建了反向悬挂结构设计模型。此外，采用深度学习来训练复杂的功能以进行性能校正，并优化壳结构的平面结构。此外，力被转化为形状，并且完成形状发现过程，以方便设计师的操作。最后，利用地理信息系统的空间锚固能力来匹配和计算空间元素的相关特征。在此基础上，采用基于权重分布的指标构建策略，实现数据融合诊断框架，增强建筑设计的智能化。仿真结果表明，物理悬挂实验的最大拉伸应力为 3.71MPa，最大压缩应力为 14.7MPa。压缩应力值远大于拉伸应力值。压缩和拉伸应力下的最大变形值差值分别为 0.07 和 0.11，误差在可接受范围内，与实际悬挂物理实验获得的压缩状态结果相似，表明基于深度学习的反向悬挂结构初始设计模型是可靠的。此外，进化优化效果分析结果验证了算法的可行性，表明设计结构的负载相对均匀。这里构建的反向悬挂结构模型的研究意义在于为建筑悬挂中一些复杂结构的反向设计提供准确可行的设计思路，还可以缩短此类结构的创作和改进周期，优化建筑结构的性能和施工周期。
