Estimating Cement Compressive Strength from Microstructure Images Using Broad Learning System

Abstract

The microstructure images of cement are often used as the main data source for estimating compressive strength. They contain ample physical properties during the hydration process. Different gray values represent different substances in the grayscale image of cement. Deep learning algorithm based on microstructure images have been proposed to estimate cement compressive strength (CCS). However, there are a large number of parameters that need to be adjusted in deep structure. The high-efficiency system named broad learning system (BLS) is tried to use to estimate the cement compressive strength. The original cement microstructure images and the extracted features are used as input respectively, the connection weights can be obtained directly by calculating pseudo inverse matrix of feature matrix of microstructure image. If the structure is not sufficient to gain suitable result, BLS only calculates the pseudo inverse matrix of additional nodes to improve accuracy. The experiment shows that the broad learning structure (BLS) is an effective and efficient method on estimating cement compressive strength by contrasting with deep learning structure.