高斯交叉熵和组织智能用于实际规模高层建筑中斜带桁架支腿系统的优化设计

IF 3 3区 工程技术 Q2 ENGINEERING, MECHANICAL Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI:10.1016/j.probengmech.2024.103616
Salar Farahmand-Tabar, Payam Ashtari, Mehdi Babaei
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引用次数: 0

摘要

本研究探讨了采用支腿和带式桁架系统的高层建筑的最佳结构设计。该研究采用了高斯交叉熵与组织智能(GCE-OI),这是一种利用自组织图作为机器学习算法的新型优化方法,以及交叉熵优化中的高斯概率分布。这种方法用于预测有希望的解决方案,并指导搜索过程以迅速收敛。优化包括构件尺寸(重量)和支腿位置(拓扑结构),同时在传统水平桁架的基础上引入斜带桁架以提高性能。优化过程包括对一个 25 层楼高、承受风荷载的真实模型进行优化,并将优化结果与多种著名算法进行比较。结果表明,在机器学习的支持下,所提出的优化器优于其他算法,可提供收敛性更强的优质解决方案。考虑到斜带桁架的效率和所提出的稳健优化方法(GCE-OI),优化布置的支腿系统最大限度地降低了建筑成本,并通过限制响应增强了结构稳定性。
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Gaussian cross-entropy and organizing intelligence for design optimization of the outrigger system with inclined belt truss in real-size tall buildings

This research explores the optimal structural design for tall buildings with an outrigger and belt truss system. The study employs Gaussian Cross-Entropy with Organizing Intelligence (GCE-OI), a novel optimization approach that utilizes a self-organizing map as a machine learning algorithm, and Gaussian probability distribution in Cross-Entropy optimization. This approach is used to predict promising solutions and to guide the search process for swift convergence. The optimization encompasses member sizing (weight) and outrigger placement (topology) while introducing inclined belt trusses alongside traditional horizontal trusses for enhanced performance. The process involves optimizing a 25-story real-size model subjected to wind load, and the results are compared against multiple well-known algorithms. The results show that the proposed optimizer, supported by machine learning, outperforms alternative algorithms, offering superior solutions with enhanced convergence. Considering the efficiency of the inclined belt trusses and the proposed robust optimization method (GCE-OI), the optimally-placed outrigger system minimizes the constructional cost and enhances structural stability by limiting the responses.

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来源期刊
Probabilistic Engineering Mechanics
Probabilistic Engineering Mechanics 工程技术-工程:机械
CiteScore
3.80
自引率
15.40%
发文量
98
审稿时长
13.5 months
期刊介绍: This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear engineering. The journal aims to maintain a healthy balance between general solution techniques and problem-specific results, encouraging a fruitful exchange of ideas among disparate engineering specialities.
期刊最新文献
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