Conventional mechanical design paradigms rely on experts systematically refining concepts through experience-guided modification and FEA to meet specific requirements. However, this approach can be time-consuming and heavily dependent on prior knowledge and experience. While numerous machine learning models have been developed to streamline this intensive and expert-driven iterative process, these methods typically demand extensive training data and considerable computational resources. Furthermore, methods based on deep learning are usually restricted to the specific domains and tasks for which they were trained, limiting their applicability across different tasks. This creates a trade-off between the efficiency of automation and the demand for resources. In this study, we present a novel approach that integrates pre-trained LLMs with a FEM module. The FEM module evaluates each design and provides essential feedback, guiding the LLMs to continuously learn, plan, generate, and optimize designs without the need for domain-specific training. We demonstrate the effectiveness of our proposed framework in managing the iterative optimization of truss structures, showcasing its capability to reason about and refine designs according to structured feedback and criteria. Our results reveal that these LLM-based agents can successfully generate truss designs that comply with natural language specifications with a success rate of up to 90%, which varies according to the applied constraints. By employing prompt-based optimization techniques we show that LLM based agents exhibit optimization behavior when provided with solution-score pairs to iteratively refine designs to meet specifications. This ability of LLM agents to produce viable designs and optimize them based on their inherent reasoning capabilities highlights their potential to develop and implement effective design strategies autonomously.

The main concern of this study is to find the optimal design of truss structures considering sizing and layout variables simultaneously. As compared to purely sizing optimization problems, this problem is more challenging since the two types of variables involved are fundamentally different in nature. In this paper, a reinforcement learning method combining the update process and Monte Carlo tree search called the update Monte Carlo tree search (UMCTS) for sizing optimization problems is applied to solve combined sizing and layout optimization for truss structures. This study proposes a novel update process for nodal coordinates with two features. (1) The allowed range of each coordinate varies in each round. (2) Accelerators for the number of entries in the allowed range and iteration numbers are introduced to reduce the computation time. Furthermore, nodal coordinates and member areas are determined at the same time with only one search tree in each round. The validation and efficiency of the UMCTS are tested on benchmark problems of planar and spatial trusses with discrete sizing variables and continuous layout variables. It is shown that the CPU time of the UMCTS is two times faster than the branch and bound method. The numerical results demonstrate that the proposed method stably achieves a better solution than other traditional methods.

Sizing optimization of truss structures is a complex computational problem, and the reinforcement learning (RL) is suitable for dealing with multimodal problems without gradient computations. In this paper, a new efficient optimization algorithm called update Monte Carlo tree search (UMCTS) is developed to obtain the appropriate design for truss structures. UMCTS is an RL-based method that combines the novel update process and Monte Carlo tree search (MCTS) with the upper confidence bound (UCB). Update process means that in each round, the optimal cross-sectional area of each member is determined by search tree, and its initial state is the final state in the previous round. In the UMCTS algorithm, an accelerator for the number of selections for member area and iteration number is introduced to reduce the computation time. Moreover, for each state, the average reward is replaced by the best reward collected on the simulation process to determine the optimal solution. The proposed optimization method is examined on some benchmark problems of planar and spatial trusses with discrete sizing variables to demonstrate the efficiency and validity. It is shown that the computation time for the proposed approach is at least ten times faster than the branch and bound (BB) method. The numerical results indicate that the proposed method stably achieves better solution than other conventional methods.

Living creatures and machines interact with the world through their morphology and motions. Recent advances in creating bio-inspired morphing robots and machines have led to the study of variable geometry truss (VGT), structures that can approximate arbitrary geometries and has large degree of freedom to deform. However, they are limited to simple geometries and motions due to the excessively complex control system. While a recent work PneuMesh solves this challenge with a novel VGT design that introduces a selective channel connection strategy, it imposes new challenge in identifying effective channel groupings and control methods. Building on top of the hardware concept presented in PneuMesh, we frame the challenge into a co-design problem and introduce a learning-based model to find a sub-optimal design. Specifically, given an initial truss structure provided by a human designer, we first adopt a genetic algorithm (GA) to optimize the channel grouping, and then couple GA with reinforcement learning (RL) for the control. The model is tailored to the PneuMesh system with customized initialization, mutation and selection functions, as well as the customized translation-invariant state vector for reinforcement learning. The result shows that our method enables a robotic table-based VGT to achieve various motions with a limited number of control inputs. The table is trained to move, lower its body or tilt its tabletop to accommodate multiple use cases such as benefiting kids and painters to use it in different shape states, allowing inclusive and adaptive design through morphing trusses.

The paper proposes a novel adaptive search space decomposition method and a novel gradient-free optimization-based formulation for the pre- and post-buckling analyses of space truss structures. Space trusses are often employed in structural engineering to build large steel constructions, such as bridges and domes, whose structural response is characterized by large displacements. Therefore, these structures are vulnerable to progressive collapses due to local or global buckling effects, leading to sudden failures. The method proposed in this paper allows the analysis of the load-equilibrium path of truss structures to permanent and variable loading, including stable and unstable equilibrium stages and explicitly considering geometric nonlinearities. The goal of this work is to determine these equilibrium stages via optimization of the Lagrangian kinematic parameters of the system, determining the global equilibrium. However, this optimization problem is non-trivial due to the undefined parameter domain and the sensitivity and interaction among the Lagrangian parameters. Therefore, we propose formulating this problem as a nonlinear, multimodal, unconstrained, continuous optimization problem and develop a novel adaptive search space decomposition method, which progressively and adaptively re-defines the search domain (hypersphere) to evaluate the equilibrium of the system using a gradient-free optimization algorithm. We tackle three benchmark problems and evaluate a medium-sized test representing a real structural problem in this paper. The results are compared to those available in the literature regarding displacement-load curves and deformed configurations. The accuracy and robustness of the adopted methodology show a high potential of gradient-free algorithms in analyzing space truss structures.