This paper uses an innovative algorithm combining machine learning as a decision-maker (DM) and Particle Swarm Optimization (PSO), called DMPSO, as a structural optimization technique, to design reinforced concrete frames for progressive collapse employing the alternate path method. In the alternate path method, multiple scenarios of removing critical elements should be considered, which makes the design process extremely repetitive and costly. Therefore, the development of an optimization technique is beneficial for producing efficient and cost-effective design solutions. The effectiveness of the proposed optimization algorithm is illustrated in optimization of a reinforced concrete structure that is subjected to lateral seismic forces, while the design concurrently satisfies both the American Concrete Institute provisions and the Unified Facilitates Criteria progressive collapse requirements. The results confirm the ability of the proposed DMPSO algorithm to efficiently find optimal design solutions in reinforced concrete structures that are subjected to progressive collapse.

本文使用一种创新的算法，将机器学习作为决策者（DM）和粒子群优化（PSO）相结合，称为DMPSO，作为一种结构优化技术，以替代路径方法设计钢筋混凝土框架进行渐进式倒塌。在替代路径方法中，应考虑多种删除关键元素的方案，这使设计过程极为重复且成本很高。因此，优化技术的发展有利于产生有效且具有成本效益的设计解决方案。提出的优化算法的有效性在承受侧向地震力的钢筋混凝土结构的优化中得到了说明，设计同时满足了美国混凝土协会的规定和统一便利标准的渐进倒塌要求。结果证实了所提出的DMPSO算法能够有效地发现钢筋混凝土结构中渐进倒塌的最佳设计方案的能力。