Machine-specified ground structures for topology optimization of binary trusses using graph embedding policy network. (September 2021)

• Record Type: Journal Article
• Authors: Zhu, Shaojun; Ohsaki, Makoto; Hayashi, Kazuki; Guo, Xiaonong
• Is Part Of: Advances in engineering software. Volume 159(2021)
• Publication Date: 2021-09
• Subjects: Topology optimization -- Reinforcement learning -- Graph embedding -- Binary trusses -- Machine-specified ground structures; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2021.103032
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Machine-specified ground structure is proposed for optimization of binary trusses. Trained agent can generate various stable ground structures with a given node-set. Various node-sets can be handled without re-training owing to graph embedding. Machine-specified ground structures are more likely to obtain global optimum. Abstract: This paper proposes the concept of machine-specified ground structures for topology optimization of trusses. Unlike general ground structures with dense and regular connectivity, machine-specified ground structures are sparse stable ground structures with a specified number of members designed by machines. Firstly, the generation process of machine-specified ground structures from a given node-set is formulated as a reinforcement learning task. Graph embedding is used to integrate the structural information into a comprehensive feature matrix to describe the state. By establishing the policy network, the probability of each action, i.e., selecting each node in the node-set, is obtained based on the comprehensive feature matrix. The task is solved using a gradient-based algorithm called REINFORCE . A randomized 4 × 4 node-set is used to train the agent. The policy converges with a high average reward, and generates different yet reasonable structures because a stochastic policy is employed. Besides, the agent can handle different-sized node-sets without re-training. Hence, the machine-specified ground structures generated by the trained agent can be utilized to assist the structural topology design. Subsequently, a method for a typical problem with singular optimal solutions, i.e., topology optimization of binary trusses with stress and displacement constraints, is proposed based on machine-specified ground structures. Finally, through different-sized numerical examples, it is demonstrated that the machine-specified ground structures lead to a variety of optimal solutions, and it is more likely to obtain the global optimum than fully-connected ground structures. It is worth noting that machine-specified ground structures can also be applied to other problems without re-training.