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Advances in Materials Science and Engineering
Volume 2018, Article ID 6387930, 16 pages
Research Article

Deep Learning Approach for Estimating Workability of Self-Compacting Concrete from Mixing Image Sequences

State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Xuehui An; nc.ude.auhgnist.liam@euxna

Received 23 May 2018; Accepted 25 October 2018; Published 25 November 2018

Academic Editor: Barbara Liguori

Copyright © 2018 Zhongcong Ding and Xuehui An. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.