Estimation of Critical Parameters in Concrete Production Using Multispectral Vision Technology

Michael Edberg Hansen, Bjarne Kjær Ersbøll, Jens Michael Carstensen, Allan Aasbjerg Nielsen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    We analyze multispectral reflectance images of concrete aggregate material, and design computational measures of the important and critical parameters used in concrete production. The features extracted from the images are exploited as explanatory variables in regression models and used to predict aggregate type, water content, and size distribution. We analyze and validate the methods on five representative aggregate types, commonly used in concrete production. Using cross validation, the generated models proves to have a high performance in predicting all of the critical parameters.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science
    Publication date2005
    Publication statusPublished - 2005

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