Interpretable Deep Neural Network Deployment for Concrete Compressive Strength Prediction

Key Points

• Concrete compressive strength predictions can be achieved through interpretable deep neural networks, improving accuracy.
• Model accuracy reaches up to 95% when applied to concrete samples across various conditions.
• Assessment using machine learning algorithms shows that interpretable models enhance user confidence in predictions.
• These findings suggest a potential for greater acceptance and application of AI in civil engineering projects.