Publications

Truss structures must satisfy mechanical requirements and practical constraints related to fabrication, transportation, and assembly. These factors make truss design a highly constrained problem, where traditional optimization methods often struggle due to high computational demands and slow convergence. This paper introduces an innovative approach that frames truss design as a sequential decision-making problem, addressed through reinforcement learning combined with generative grammar rules. By emulating a continuous human-computer interaction and effectively balancing the exploration and exploitation of design alternatives, this method outperforms traditional techniques in computational efficiency for structural design tasks.