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<p>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<sub>2</sub>) 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<sub>2</sub> 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.</p>

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Intelligent low carbon reinforced concrete beam design optimization via deep reinforcement learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Alireza Hosseinzadeh,
  • Mehdi Dehestani

摘要

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 (CO2) 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 CO2 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.