A method for real-time moisture estimation based on self-compacting concrete workability detected during the mixing process

Ding, Zhongcong; An, Xuehui

doi:10.1016/j.conbuildmat.2017.02.047

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…For example, Daumann and Nirschl used ultramarine blue as a tracer component and determined the homogeneity of mixtures during the concrete-mixing process [7]. Li et al developed an image processing method to determine the SF and VF values of SCC during the mixing process [8,9]. However, the methods based on image processing rely heavily on human experience and insight.…”

Section: Introductionmentioning

confidence: 99%

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

Ding

2018

Advances in Materials Science and Engineering

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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.

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Section: Introductionmentioning

confidence: 99%

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

Ding

2018

Advances in Materials Science and Engineering

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Experimental Study on the Effect of Mixing Time on the Performance of High Strength Concrete

Ma,

Zhu,

Zeng

et al. 2024

Lecture Notes in Civil Engineering

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To solve the problem of concrete quality caused by non-standard mixing time of high-strength concrete and improve the durability life of concrete structures, based on the working performance and mechanical properties of concrete, effect of mixing time on the workability and strength was studied. In this paper, for high-strength concrete with water-binder ratios of 0.31 and 0.38, the mixing time was 120 s, 160 s, 180 s and 240 s. The effects of different mixing times on the fluidity, cohesion and water retention of high-strength concrete and the compressive strength of 28 days were studied. The test results show that the appropriate mixing time benefits the working performance of concrete and the development of concrete strength. Based on the workability and mechanical properties of concrete, the mixing time of concrete with a water-binder ratio of 0.38 is 160 s, and the mixing time of concrete with a water-binder ratio of 0.31 is 180 s. To ensure the excellent uniformity, compactness, workability and mechanical properties of concrete, the mixing time should consider the slump and the water-binder ratio of concrete. For concrete with a water-binder ratio of 0.31 to 0.38, the mixing time can change from 180 to 160 s.

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Production and placement of self-compacting concrete

Kashani

Ngo

2020

Self-Compacting Concrete: Materials, Properties and Applications

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A method for real-time moisture estimation based on self-compacting concrete workability detected during the mixing process

Cited by 7 publications

References 27 publications

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

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

Experimental Study on the Effect of Mixing Time on the Performance of High Strength Concrete

Production and placement of self-compacting concrete

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