Development of an Optimization Algorithm for Designing Low-Carbon Concrete Materials Standardization with Blockchain Technology and Ensemble Machine Learning Methods
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| Publicado en: | Buildings vol. 15, no. 16 (2025), p. 2809-2830 |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/buildings15162809 |2 doi | |
| 035 | |a 3243994111 | ||
| 045 | 2 | |b d20250815 |b d20250831 | |
| 084 | |a 231437 |2 nlm | ||
| 100 | 1 | |a Ebenezer, Nwetlawung Zilefac | |
| 245 | 1 | |a Development of an Optimization Algorithm for Designing Low-Carbon Concrete Materials Standardization with Blockchain Technology and Ensemble Machine Learning Methods | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study presents SmartMix Web3, a framework combining ensemble machine learning and blockchain technology to optimize low-carbon concrete design. It addresses two key challenges: (1) the limitations of conventional models in predicting concrete performance, and (2) ensuring data reliability and overcoming collaboration issues in AI-driven sustainable construction. Validated with 61 real-world experiments in Cameroon and 752 mix designs, the framework shows major improvements in predictive accuracy and decentralized trust. To address the first research question, a stacked ensemble model comprising Extreme Gradient Boosting (XGBoost)–Random Forest and a Convolutional Neural Network (CNN) was developed, achieving a 22% reduction in Root Mean Square Error (RMSE) for compressive strength prediction and embodied carbon estimation compared to traditional methods. The 29% reduction in Mean Absolute Error (MAE) results confirms the superiority of Extreme Learning Machine (EML) in low-carbon concrete performance prediction. For the second research question, SmartMix Web3 employs blockchain to ensure tamper-proof traceability and promote collaboration. Deployed on Ethereum, it automates verification of tokenized Environmental Product Declarations via smart contracts, reducing disputes and preserving data integrity. Federated learning supports decentralized training across nine batching plants, with Secure Hash Algorithm (SHA)-256 checks ensuring privacy. Field implementation in Cameroon yielded annual cost savings of FCFA 24.3 million and a 99.87 kgCO2/m3 reduction per mix design. By uniting EML precision with blockchain transparency, SmartMix Web3 offers practical and scalable benefits for sustainable construction in developing economies. | |
| 653 | |a Green development | ||
| 653 | |a Carbon content | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Collaboration | ||
| 653 | |a Deep learning | ||
| 653 | |a Performance prediction | ||
| 653 | |a Algorithms | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Blockchain | ||
| 653 | |a Construction contracts | ||
| 653 | |a Machine learning | ||
| 653 | |a Carbon | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Viscosity | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Concrete curing | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Neural networks | ||
| 653 | |a Sustainability | ||
| 653 | |a Variables | ||
| 653 | |a Construction industry | ||
| 653 | |a Cement | ||
| 653 | |a Federated learning | ||
| 653 | |a Design optimization | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Compressive strength | ||
| 700 | 1 | |a Yi-Hsin, Lin | |
| 773 | 0 | |t Buildings |g vol. 15, no. 16 (2025), p. 2809-2830 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3243994111/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3243994111/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3243994111/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |