Prediction of optimized concrete mixture using PSO

1. The collected data will consist of recycled aggregate concrete mixture design examples, with 8 input features and one output
Input Feature (attributes)
Water-to-cement ratio
Cement content
Sand content (Fine aggregate)
Recycled aggregate content (recycled coarse aggregate)
Gavel content (coarse aggregate)
Superplasticizer
Age
Specimen type
Output
Compressive strength

2. Feature normalization with any new techniques
3. Cross-validation with any new techniques randomly divide the data into training and testing sets using 70%-30%
4. Hyperparameter Tuning
5. Model Development: 4 Deep learning new techniques for prediction
6. Mixture Optimization with techniques better than Particle swarm optimization
7. Programing environment Python
8. Simple explanation for each code activity.
9. Each stage’s results should be shown in figures, tables and curves to show the training and loss in accuracy