Gaussian cross-entropy and organizing intelligence for design optimization of the outrigger system with inclined belt truss in real-size tall buildings

• Published in: Probabilistic engineering mechanics 2024-04, Vol.76, p.103616, Article 103616
• Main Authors: Farahmand-Tabar, Salar, Ashtari, Payam, Babaei, Mehdi
• Format: Article
• Language: English
• Subjects: Gaussian cross-entropy; Inclined belt truss; Machine learning; Outrigger system; Size and topology optimization
• ISSN: 0266-8920; 1878-4275
• DOI: 10.1016/j.probengmech.2024.103616

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. •Innovative Cross-Entropy optimization with Gaussian probability distribution.•Organizing intelligence for data clustering and prediction of promising solutions.•Improving the performance of the outrigger system with inclined belt truss.•Simultaneous size and topology optimization for structural efficiency.•Better efficiency of ML-driven Gaussian Cross-Entropy optimization among competitors.