Paper Info
Title
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.
Year
DOI
Venue
2018
10.1109/SMC.2018.00716
2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Keywords
Field
DocType
Broad Learning System, Cement Compressive Strength, Microstructure Images, Deep Learning
Data source,Microstructure,Computer science,Control theory,Compressive strength,Moore–Penrose pseudoinverse,Algorithm,Artificial intelligence,Feature matrix,Deep learning,Cement,Grayscale
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yonghao Dang100.34
Lin Wang216227.96
Jianqin Yin312.73
Xuehui Zhu432.06
Zhiquan Feng53613.73
Jifeng Guo6515.53