Estimating cement compressive strength using three-dimensional microstructure images and deep belief network. (February 2020)

• Record Type: Journal Article
• Authors: Guo, Jifeng; Li, Meihui; Wang, Lin; Yang, Bo; Zhang, Liangliang; Chen, Zhenxiang; Han, Shiyuan; Garcia-Hernandez, Laura; Abraham, Ajith
• Is Part Of: Engineering applications of artificial intelligence. Volume 88(2020)
• Publication Date: 2020-02
• Subjects: Cement compressive strength -- Microstructure images -- Deep belief network -- Graphics processing units; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.103378
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The estimation of cement compressive strength is of great significance in the quality inspections, technological designs, and engineering applications for cement. Compared to destructive methods, the nondestructive estimation approaches save the cost in the manpower and material. However, the existing nondestructive methods have the large error because the used influence factors are difficult to control and the used two-dimensional microstructure images can not reflect the specific spatial structure of the entire cement. In this paper, a novel model is proposed to estimate the cement compressive strength using three-dimensional microstructure images and deep belief network. To reduce the computation consumption induced by three-dimensional images with abundant information, this method extracts image features that reflect the cement hydration state to estimate cement compressive strength. Deep belief network is applied to build the estimation model. Its unique training pattern and flexibility of parameters improve the ability to learn nonlinear relationships between microstructure images and cement compressive strength. Furthermore, the training processes are accelerated on the graphics processing units. The experimental results prove that the proposed method estimates cement compressive strength nondestructively and improves the efficiency.