Machine learning models to predict sewer concrete strength exposed to sulfide environments: unveiling the superiority of Bayesian-optimized prediction models

Engineering

• Learning System 100%
• Random Forest 100%
• Concrete Strength 100%
• Artificial Neural Network 66%
• Concrete Compressive Strength 66%
• Support Vector Machine 33%
• Engineering 33%
• Simple Model 33%
• Fitness Function 33%
• Concrete (Composite Building Material) 33%
• Graphical User Interface 33%
• Great Influence 33%
• Reinforced Concrete 33%
• Dependability 33%
• Nonlinear Model 33%

Material Science

• Compressive Strength 100%
• Reinforced Concrete 50%
• Concrete (Composite Building Material) 50%