Genetically Optimized BP Neural Network Based Alkali Inspired Material Proportioning Design
Publication: International Conference on Road and Airfield Pavement Technology 2023
ABSTRACT
Alkali-activated materials are significant functional materials widely utilized in industrial and technological sectors. They efficiently handle solid waste, reduce cement consumption, and play a crucial role in sustainable construction. However, due to their complex reaction mechanisms and differences from traditional cementitious materials, further research is necessary to explore optimal proportioning methods that fully harness their advantages. In this study, machine learning techniques were employed to rapidly and effectively design alkali-activated material mixtures. With a total of 47 samples, 27 were used for training and 20 for testing. The BP neural network model was optimized, and analysis using the random forest algorithm revealed that the CaO content contributes the most to compressive strength. This research provides novel insights and practical guidance for alkali-activated material proportioning, reducing experimental costs, and expanding the application of machine learning in the engineering field.
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Information & Authors
Information
Published In
International Conference on Road and Airfield Pavement Technology 2023
Pages: 106 - 118
History
Published online: Feb 6, 2024
ASCE Technical Topics:
- Alkalinity and acidity
- Artificial intelligence and machine learning
- Business management
- Cement
- Chemical properties
- Chemistry
- Compressive strength
- Computer programming
- Computing in civil engineering
- Concrete
- Construction wastes
- Engineering fundamentals
- Engineering materials (by type)
- Environmental engineering
- Material mechanics
- Material properties
- Materials engineering
- Models (by type)
- Neural networks
- Optimization models
- Pollutants
- Practice and Profession
- Solid wastes
- Strength of materials
- Sustainable development
- Wastes
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