Hosseinzadeh Alireza, Dehestani Mehdi
Faculty of Civil Engineering, Babol Noshirvani University of Technology, 484, Babol, 47148-71167, Iran.
Sci Rep. 2025 Sep 26;15(1):33143. doi: 10.1038/s41598-025-18543-4.
Reinforced concrete (RC) beam design currently faces significant challenges from the substantial carbon footprint of cementitious materials and the lack of practical automated tools for simultaneous structural-environmental optimization. To address this, this paper proposes an innovative two-stage framework by utilizing artificial neural networks (ANNs) and deep reinforcement learning (RL) to automate the design of sustainable and low-carbon RC beams. In the first stage, following a comprehensive analysis of 14 machine learning algorithms, an ANN was selected for its superior predictive accuracy. The trained ANN effectively predicts concrete compressive strength (coefficient of determination, R² ≈ 0.85) and carbon dioxide (CO) emissions (R² ≈ 0.99), critical parameters for subsequent optimization, exhibiting a loss function value of 0.15 and a mean absolute error of 0.38. The second stage involves the decision-making process for designing RC beams through deep RL, utilizing a Proximal Policy Optimization (PPO) as an agent. The agent operates within a 13-dimensional parametric action space, encompassing geometric and material composition variables, and interacts with a 26-variable state space to balance structural integrity with environmental sustainability. A customized RL environment was created to optimize designs for minimal CO emissions and evaluate compliance with ACI 318 - 19 flexural design criteria. The resulting framework demonstrates comprehensive sustainability achievements, with comparative benchmarking showing PPO-optimized designs yield 43.35-75.04% lower CO₂ emissions than those from an Advantage Actor-Critic (A2C) agent, alongside automated ACI 318 - 19 code compliance, optimized utilization of supplementary cementitious materials (SCMs), and improved structural efficiency through intelligent geometric parameter selection. The code is available as open source, and a web-based interface facilitates the dissemination of research outcomes.
钢筋混凝土（RC）梁设计目前面临着重大挑战，这源于胶凝材料巨大的碳足迹以及缺乏用于同时进行结构-环境优化的实用自动化工具。为解决这一问题，本文提出了一种创新的两阶段框架，通过利用人工神经网络（ANN）和深度强化学习（RL）来实现可持续低碳RC梁设计的自动化。在第一阶段，在对14种机器学习算法进行全面分析之后，选择了人工神经网络，因为其具有卓越的预测精度。经过训练的人工神经网络能够有效地预测混凝土抗压强度（决定系数，R²≈0.85）和二氧化碳（CO）排放量（R²≈0.99），这是后续优化的关键参数，其损失函数值为0.15，平均绝对误差为0.38。第二阶段涉及通过深度强化学习进行RC梁设计的决策过程，使用近端策略优化（PPO）作为智能体。该智能体在一个13维的参数动作空间内运行，该空间包含几何和材料组成变量，并与一个26变量的状态空间进行交互，以平衡结构完整性和环境可持续性。创建了一个定制的强化学习环境，以优化设计，实现最低的CO排放量，并评估是否符合美国混凝土学会（ACI）318 - 19弯曲设计标准。由此产生的框架展示了全面的可持续性成果，对比基准测试表明，与优势行动者-评论家（A2C）智能体相比，经PPO优化的设计可减少43.35 - 75.04%的CO₂排放量，同时实现自动符合ACI 318 - 19规范、优化辅助胶凝材料（SCMs）的使用，并通过智能几何参数选择提高结构效率。该代码作为开源代码提供，基于网络的界面便于研究成果的传播。
