Case Optimization of Recycled Concrete using Deep Learning

Engaging in the study and advocacy of regenerated concrete aligns with the fundamental national policy of resource conservation and environmental protection. Assisting the transformation and upgrading of our country's construction industry to achieve green, high-quality development is highly significant. Due to the rapid advancement of engineering construction technology, there is an increasing demand for concrete materials. The data were extracted and standardized based on key variables such as the source of test data, material information, test environment in- formation, and quantitative index of material properties. Multicollinearity diagnosis was used to determine the main influencing factors of material properties.