Max Meyer, Amadeus Langer, Max Mehltretter, Dries Beyer, Max Coenen, Tobias Schack, Michael Haist, Christian Heipke
{"title":"Fresh Concrete Properties from Stereoscopic Image Sequences","authors":"Max Meyer, Amadeus Langer, Max Mehltretter, Dries Beyer, Max Coenen, Tobias Schack, Michael Haist, Christian Heipke","doi":"10.1007/s41064-024-00303-0","DOIUrl":null,"url":null,"abstract":"<p>Increasing the degree of digitization and automation in concrete production can make a decisive contribution to reducing the associated <span>\\(\\text{CO}_{2}\\)</span> 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.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s41064-024-00303-0","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
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.
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).