ArticleScientific reports2025
Intelligent low carbon reinforced concrete beam design optimization via deep reinforcement learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Cross-Modal Graph Attention for Bridge SHM Data Imputation.Sensors (Basel, Switzerland) · 2026Article
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2 authors.
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Abstract
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
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