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

Ding, Zhongcong; An, Xuehui

doi:10.1155/2018/6387930

Cited by 16 publications

(6 citation statements)

References 32 publications

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“…A research approach for fresh concrete quality monitoring was proposed in [21], where the concrete mix proportion is determined from images of fresh concrete using a convolutional neural network (CNN). In [22], an approach for determining the workability from image sequences acquired during the mixing process using a LSTM deep learning network has been postulated. While promising results were obtained, processing was done on rather low resolution grey scale images only and the approach relied on 2D transformations, ignoring the clearly visible effects perspective distortions.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Concrete 4.0 ‐ Sustainable concrete construction with digital quality control

Haist,

Schack,

Coenen

et al. 2023

ce papers

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Concrete is a mass commodity with more than approx. 250 Mio. batches of concrete being produced every year in Europe alone. Despite this enormous repetition rate, both the quality inspection of the concrete raw materials as well as of the mixed concrete are still primarily batch‐based, relying on manual processes, thus hindering the application of modern process control schemes. The reasons for this lack are on the one hand to be seen in the lack of suitable sensor technologies and on the other hand in missing control algorithms. The paper at hand gives an overview on the current state of the art sensor technologies for both raw material as well as concrete properties measurement. Further, it outlines a pathway towards an online concrete process control.

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Section: Related Work and Backgroundmentioning

confidence: 99%

Concrete 4.0 ‐ Sustainable concrete construction with digital quality control

Haist,

Schack,

Coenen

et al. 2023

ce papers

View full text Add to dashboard Cite

show abstract

“…Both, (Li and An, 2014) and (Ding and An, 2018) evaluate images of concrete from the mixing process in a single-shaft mixer based on its workability. While (Li and An, 2014) use classical image analysis methods and determine the slump flow values and the V-funnel flow time by extracting the shape of the concrete in the mixer using pre-defined features, (Ding and An, 2018) show that it is also possible to determine the two values using Deep Learning, the method is thus independent of human experience and insights into the appearance of concrete with different rheological properties, once training data have been acquired.…”

Section: Related Workmentioning

confidence: 99%

Image-Based Deep Learning for Rheology Determination of Bingham Fluids

Ponick¹,

Langer²,

Beyer

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this work, a method to predict the rheological properties of ultrasonic gel, as a reference substance of cement paste, is presented. For this purpose, images are taken with a stereo camera system which show a mixing paddle moving through the ultrasonic gels of different consistency, thus setting them in motion. A digital elevation model (DEM) and a corresponding orthophoto are created from the image pairs using classical image matching and orthoprojection methods. These are used as inputs into a Convolutional Neural Network (CNN), which predicts the support points of a flow curve which classically have to be determined in a rheometer in the laboratory. A simple network architecture consisting of a small number of convolution layers is compared with a pre-trained ResNet-18, which is fine-tuned using gel images. In a second series of experiments, rheological parameters, which alternatively need to be deduced from the flow curve in a separate step, are determined directly from the images. In the third series of experiments, the influence of different factors is tested, such as the position of the cameras relative to the direction of paddle movement and the importance of the DEMs and orthophotos in the training. It is shown in this paper that it is possible to predict the rheological properties of the ultrasonic gels with a suitable setup with a satisfying accuracy.

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“…Several deep learning 13–15 and machine learning methods 16–19,31 are conducted to improve the durability, drying shrinkage, slump model, and so on. In our proposed approach, we utilize the SCC using foundry sand hybrid fibers to enhance durability and sustainability.…”

Section: Introductionmentioning

confidence: 99%

“…The current fire test outcomes show that the SCC has a better withstanding capacity to fire than the normal concrete. [9][10][11][12] Several deep learning [13][14][15] and machine learning methods [16][17][18][19]31 are conducted to improve the durability, drying shrinkage, slump model, and so on. In our proposed approach, we utilize the SCC using foundry sand hybrid fibers to enhance durability and sustainability.…”

Section: Introductionmentioning

confidence: 99%

A novel strength behavior prediction model of self‐compacting concrete with foundry sand and hybrid fibers

Aruchamy¹,

Ramasamy²,

Venkatraman³

2021

Structural Concrete

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In this paper, an Improved FireFly Optimization Algorithm (IFFOA)‐based optimal Self‐Organizing Polynomial Neural Network (SOPNN) is proposed to analyze the compressive strength, tensile strength, and flexural strength of the self‐compacting concrete (SCC) with the inclusion of hybrid fibers whenever exposed to high temperature. The SCC mixture is formed by the materials taken in this study along with the hybrid fibers. The preparation of SCC is made by adding water to powder with a proportion of 3:10 and hybrid fibers content is about 2 kg/m3. The samples are heated to a temperature of 210, 320, 530, and 790° at the duration of 7 and 28 days. Experiments are performed to analyze the loss in compressive strength, tensile strength, and flexural strength. The proposed method is used to analyze the loss functions based on the amount of cement, aggregates, temperature, additives along with the inclusion, and exclusion of hybrid fibers. Further, it is found the proposed method analyzes the loss in compressive strength, tensile strength, and flexural strength of SCC more accurately than the other methods.

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

Cited by 16 publications

References 32 publications

Concrete 4.0 ‐ Sustainable concrete construction with digital quality control

Concrete 4.0 ‐ Sustainable concrete construction with digital quality control

Image-Based Deep Learning for Rheology Determination of Bingham Fluids

A novel strength behavior prediction model of self‐compacting concrete with foundry sand and hybrid fibers

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