Fresh Concrete Properties from Stereoscopic Image Sequences

IF 2.1 4区 地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Max Meyer, Amadeus Langer, Max Mehltretter, Dries Beyer, Max Coenen, Tobias Schack, Michael Haist, Christian Heipke
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Abstract

Increasing the degree of digitization and automation in concrete production can make a decisive contribution to reducing the associated \(\text{CO}_{2}\) emissions. This paper presents a method which predicts the properties of fresh concrete during the mixing process on the basis of stereoscopic image sequences of the moving concrete and mix design information or a variation of these. A Convolutional Neural Network (CNN) is used for the prediction, which receives the images supported by information about the mix design as input. In addition, the network receives temporal information in the form of the time difference between image acquisition and the point in time for which the concrete properties are to be predicted. During training, the times at which the reference values were captured are used for the latter. With this temporal information, the network implicitly learns the time-dependent behavior of the concrete properties. The network predicts the slump flow diameter, the yield stress and the plastic viscosity. The time-dependent prediction opens up the possibility of forecasting the temporal development of the fresh concrete properties during mixing. This is a significant advantage for the concrete industry, as countermeasures can then be taken in a timely manner, if the properties deviate from the desired ones. In various experiments it is shown that both the stereoscopic observations and the mix design information contain valuable information for the time-dependent prediction of the fresh concrete properties.

Abstract Image

从立体图像序列中获取新鲜混凝土特性
提高混凝土生产的数字化和自动化程度可以为减少相关排放做出决定性贡献。本文介绍了一种方法,该方法可根据移动混凝土的立体图像序列和混合设计信息或这些信息的变体,预测搅拌过程中新拌混凝土的特性。预测使用了卷积神经网络 (CNN),该网络接收由混合设计信息支持的图像作为输入。此外,该网络还能接收时间信息,即图像采集与预测混凝土特性的时间点之间的时间差。在训练过程中,参考值的采集时间被用于后者。有了这些时间信息,网络就能隐式地学习随时间变化的混凝土特性行为。该网络可预测坍落度流动直径、屈服应力和塑性粘度。随时间变化的预测为预报搅拌过程中新拌混凝土性能的随时间变化提供了可能。这对混凝土行业来说是一个重大优势,因为如果性能偏离预期,就可以及时采取应对措施。各种实验表明,立体观测和混合设计信息都包含了对新拌混凝土性能随时间变化进行预测的宝贵信息。
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来源期刊
CiteScore
8.20
自引率
2.40%
发文量
38
期刊介绍: PFG is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the interconnected field of geoinformation science. It places special editorial emphasis on the communication of new methodologies in data acquisition and new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general. The journal hence addresses both researchers and students of these disciplines at academic institutions and universities as well as the downstream users in both the private sector and public administration. Founded in 1926 under the former name Bildmessung und Luftbildwesen, PFG is worldwide the oldest journal on photogrammetry. It is the official journal of the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF).
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