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

Abstract

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 has significant implications for quality inspections and the design of high-performance products. However, existing measurement technology remains traditional and destructive. Except for the waste of various resources and high time-consuming, it requires significant improvement since the unprofessional operations will give rise to large errors. In this paper, 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 strength, and the broad learning system distills the potential correlation between the strength and those factors. It can nondestructively estimate the strength directly with the internal factors, which saves labor power and material, avoids large errors caused by unprofessional operations, and aids high-performance cement production.