Development of High-Strength Concrete Mixed Design System Using Artificial Intelligence

Abstract

The quality inspection of high-strength concrete construction sites consists of a compressive strength test that is considered the most important, but this can be confirmed through a compressive strength test after 28 days of high-strength concrete application. Therefore, it is of paramount importance to ship high-quality products to ready-mixed concrete factories by increasing the reliability of the mixed design that affects high-strength concrete production. In addition, there is a need to develop an efficient management system for mixed design that determines high-strength concrete quality by measuring the mixing ratio of materials in the ready-mixed concrete factory production stage. This study used matrix laboratory(MATLAB) using Deep learning, a language that performs mathematics and engineering calculations based on matrices, and presented a mixed design model by adjusting the strength through input and output variables, learning data collection, model structure determination, learning error, and repetition results. The predicted mean value of 40 MPa was measured at 40.75 MPa, showing a difference of 0.75 MPa and 40 MPa, and the error rate was confirmed to be 4.13%. And the predicted mean value of 55 MPa was measured as 55.55 MPa, showing a difference between 55 MPa and 0.55 MPa, and the error rate was confirmed to be 1.73%. Through this study, the reliability of high-strength concrete quality management is secured by applying a high-strength concrete mixed design system using artificial intelligence(AI) and adjusting it in connection with all fields of the production process.