An Efficient Inspection System Based on Broad Learning: Nondestructively Estimating Cement Compressive Strength With Internal Factors

Cement has been widely used in civil engineering, whose quality directly affects the safety of buildings. Cement compressive strength, as an important quality indicator, its accurate estimation is of great significance in quality inspections and the design of high-performance products. However, existing measurement technology remains traditional and destructive. Except for high time-consuming and the waste of various resources, it requires significant improvement since the unprofessional operations will give rise to large errors. In this article, an efficient system is proposed to estimate the cement compressive strength based on the broad learning and internal factors, in which the index system describes the internal factors affecting the compressive strength, and the broad learning system distills the potential correlation between the compressive strength and those factors. It can nondestructively estimate the strength directly with the internal factors, e.g., clinker composition and physical properties. In addition, to verify its practicability and to assist the formula optimization in the application, the robustness test and factorial analysis are designed. The experimental results prove that this model can accurately estimate the strength with excellent generalization ability, which saves labor power and material, avoids large errors caused by unprofessional operations, and aids high-performance cement production. Especially, its ability to rapidly build an accurate estimation model is beneficial for the production of various cement in industry.