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Open AccessArticle
A Hybrid Deep Reinforcement Learning Architecture for Optimizing Concrete Mix Design Through Precision Strength Prediction
by
Ali Mirzaei
Ali Mirzaei 1
and
Amir Aghsami
Amir Aghsami 2,*
1
Department of Civil Engineering, College of Engineering, Yazd Branch, Islamic Azad University, Yazd 89168-71967, Iran
2
Department of Industrial Engineering, Ankara Yıldırım Beyazıt University, 06010 Ankara, Turkey
*
Author to whom correspondence should be addressed.
Math. Comput. Appl. 2025, 30(4), 83; https://doi.org/10.3390/mca30040083 (registering DOI)
Submission received: 29 June 2025
/
Revised: 23 July 2025
/
Accepted: 30 July 2025
/
Published: 3 August 2025
Abstract
Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R2 of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments.
Share and Cite
MDPI and ACS Style
Mirzaei, A.; Aghsami, A.
A Hybrid Deep Reinforcement Learning Architecture for Optimizing Concrete Mix Design Through Precision Strength Prediction. Math. Comput. Appl. 2025, 30, 83.
https://doi.org/10.3390/mca30040083
AMA Style
Mirzaei A, Aghsami A.
A Hybrid Deep Reinforcement Learning Architecture for Optimizing Concrete Mix Design Through Precision Strength Prediction. Mathematical and Computational Applications. 2025; 30(4):83.
https://doi.org/10.3390/mca30040083
Chicago/Turabian Style
Mirzaei, Ali, and Amir Aghsami.
2025. "A Hybrid Deep Reinforcement Learning Architecture for Optimizing Concrete Mix Design Through Precision Strength Prediction" Mathematical and Computational Applications 30, no. 4: 83.
https://doi.org/10.3390/mca30040083
APA Style
Mirzaei, A., & Aghsami, A.
(2025). A Hybrid Deep Reinforcement Learning Architecture for Optimizing Concrete Mix Design Through Precision Strength Prediction. Mathematical and Computational Applications, 30(4), 83.
https://doi.org/10.3390/mca30040083
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