Deep Learning Approach for Estimating Workability of Self-Compacting Concrete from Mixing Image Sequences. (25th November 2018)

• Record Type: Journal Article
• Authors: Ding, Zhongcong; An, Xuehui
• Other Names: Liguori Barbara Academic Editor.
• Is Part Of: Advances in materials science and engineering. Volume 2018(2018)
• Publication Date: 2018-11-25
• Subjects: Materials science -- Periodicals; Materials science; Periodicals; 620.11
• DOI: 10.1155/2018/6387930
• Languages: English
• ISSNs: 1687-8434

Abstract

Abstract : We propose a deep learning approach to better utilize the spatial and temporal information obtained from image sequences of the self-compacting concrete- (SCC-) mixing process to recover SCC characteristics in terms of the predicted slump flow value (SF) and V-funnel flow time (VF). The proposed model integrates features of the convolutional neural network and long short-term memory and is trained to extract features and compute an estimate. The performance of the method is evaluated using the testing set. The results indicate that the proposed method could potentially be used to automatically estimate SCC workability.