AlphaTruss: Monte Carlo Tree Search for Optimal Truss Layout Design

Abstract

Truss layout optimization under complex constraints has been a hot and challenging problem for decades that aims to find the optimal node locations, connection topology between nodes, and cross-sectional areas of connecting bars. Monte Carlo Tree Search (MCTS) is a reinforcement learning search technique that is competent to solve decision-making problems. Inspired by the success of AlphaGo using MCTS, the truss layout problem is formulated as a Markov Decision Process (MDP) model, and a 2-stage MCTS-based algorithm, AlphaTruss, is proposed for generating optimal truss layout considering topology, geometry, and bar size. In this MDP model, three sequential action sets of adding nodes, adding bars, and selecting sectional areas greatly expand the solution space and the reward function gives feedback to actions according to both geometric stability and structural simulation. To find the optimal sequential actions, AlphaTruss solves the MDP model and gives the best decision in each design step by searching and learning through MCTS. Compared with existing results from the literature, AlphaTruss exhibits better performance in finding the truss layout with the minimum weight under stress, displacement, and buckling constraints, which verifies the validity and efficiency of the established algorithm.

Integrated Schematic Design Method for Shear Wall Structures: A Practical Application of Generative Adversarial Networks

“…In terms of physical performance, the S IDR is 0.9602, and the inter-story drift is within the 1/1000 limit specified in the code [29]. It is noteworthy that with the help of the parametric model, the structural design can be manually adjusted by engineers and automatically optimized by algorithms [31,32] in the future. Furthermore, to illustrate the superiority of StructGAN-PHY adopted in this study, its evaluation results are compared with those of a data-driven GAN, i.e., StructGAN [10], as shown in Figure 12 Moreover, in terms of design efficiency, the times required for a competent engineer, StructGAN-PHY, and the proposed method to complete the schematic design of an RC shear wall structure are presented in Table 2.…”

Analysis of the Influence of Sports on College Students’ Music Vocalization Training Based on Monte Carlo and Dynamic Adjustment Factor Algorithm

“…In this paper, the improvement of vocal training methods: on the one hand, adjust the sports content, set the vocal training standards w , the corresponding scores λ , and according to the above four strategies for random selection, to achieve multiple analyses of sports training content. In the later period of exercise content adjustment, gradually strengthen the depth of research, realize the diversity of vocal training, and improve the comprehensiveness of analysis results [ 15 ]. On the other hand, balance the global judgment and local judgment ability of vocal training and integrate inertia factor Δ ν i and moderate function Z ( d i , b i ) to realize the best fusion of exercise content and training effect.…”

“…In this paper, the improvement of vocal training methods: on the one hand, adjust the sports content, set the vocal training standardsw, the corresponding scoresλ, and according to the above four strategies for random selection, to achieve multiple analyses of sports training content. In the later period of exercise content adjustment, gradually strengthen the depth of research, realize the diversity of vocal training, and improve the comprehensiveness of analysis results [15].…”

Application of Emerging Technologies to Improve Construction Performance

“…Luo et al [23] solved the truss layout optimization problem by modelling it as a Markov Decision Process (MDP) problem and using a reinforcement learning-based algorithm. A two-stage Monte Carlo Tree Search (MCTS)-based algorithm, AlphaTruss, was adopted, by first finding an action sequence to form an optimal layout and then refining the layout.…”