LEADER 02664aam 2200481I 450 001 9910711380803321 005 20160926090653.0 024 8 $aGOVPUB-C13-a9e7a8c8f42b4d635b93b79fd8376166 035 $a(CKB)5470000002482220 035 $a(OCoLC)958885834 035 $a(EXLCZ)995470000002482220 100 $a20160921d2016 ua 0 101 0 $aeng 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aMicroChar $ean application for quantitative analysis of cement and clinker microstructure images /$fJeffrey W. Bullard 210 1$aGaithersburg, MD :$cU.S. Dept. of Commerce, National Institute of Standards and Technology,$d2016. 215 $a1 online resource (21 pages) $cillustrations (color) 225 1 $aNIST technical note ;$v1876 300 $aApril 2016. 300 $aContributed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aTitle from PDF title page (viewed April 30, 2016). 320 $aIncludes bibliographical references. 330 3 $aAccurate quantitative data on cement and clinker microstructure images can prove valuable for monitoring and controlling the manufacturing of cement-based powders. Furthermore, quantitative characterization of microstructure is an essential input to microstructure-based computer models of cementitious material processing and properties. This document describes the use and operating principles of MicroChar, a computer application for automatically calculating a range of microstructural properties from an indexed 2D image. Among the properties calculated are the volume fraction, mass fraction, and surface area fraction of each phase in the image, as well as two-point correlation functions for quantifying the spatial distribution of the phases throughout the structure. The application also enables the user to package the data obtained on cement powders for uploading to the Virtual Cement and Concrete Testing Laboratory Consortium (VCCTL) software. 517 $aMicroChar 606 $aBuilding materials industry$xAppropriate technology 606 $aCement 606 $aComputer simulation 615 0$aBuilding materials industry$xAppropriate technology. 615 0$aCement. 615 0$aComputer simulation. 700 $aBullard$b Jeffrey W$01390864 701 $aBullard$b Jeffrey F$01390865 712 02$aNational Institute of Standards and Technology (U.S.).$bEngineering Laboratory. 801 0$bNBS 801 1$bNBS 801 2$bGPO 801 2$bNBS 906 $aBOOK 912 $a9910711380803321 996 $aMicroChar$93443999 997 $aUNINA LEADER 01257nam 22004333 450 001 9910900180403321 005 20241023080342.0 010 $a1-119-86338-4 010 $a1-119-86340-6 010 $a1-119-86339-2 035 $a(MiAaPQ)EBC31733449 035 $a(Au-PeEL)EBL31733449 035 $a(CKB)36378993700041 035 $a(EXLCZ)9936378993700041 100 $a20241023d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFinancial Data Analytics with Machine Learning, Optimization and Statistics 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2024. 210 4$dİ2024. 215 $a1 online resource (941 pages) 225 1 $aWiley Finance Series 311 $a1-119-86337-6 410 0$aWiley Finance Series 676 $a332.0285 700 $aChen$b Sam$01767274 701 $aCheung$b Ka Chun$01767275 701 $aYam$b Phillip$01767276 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910900180403321 996 $aFinancial Data Analytics with Machine Learning, Optimization and Statistics$94212269 997 $aUNINA