About: Prediction of Concrete Strength using Floating Centroids Method     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • Concrete is viewed as the most important cementbased composite material in the field of civil engineering. Its strength is considered the most important among its mechanical properties. Although the value of strength can be directly forecasted, the estimation of strength grade remains particularly important because concrete mortar is non-uniform, and practical preparation and curing cannot be fully simulated under laboratory conditions. In this paper, concrete strength grade was predicted by using the floating centroids method neural network classifier, which removes the fixed-centroid constraint and increases the possibility of finding an optimal neural network. Experimental results show that concrete strength prediction performance is improved by employing the floating centroids method.
  • Concrete is viewed as the most important cementbased composite material in the field of civil engineering. Its strength is considered the most important among its mechanical properties. Although the value of strength can be directly forecasted, the estimation of strength grade remains particularly important because concrete mortar is non-uniform, and practical preparation and curing cannot be fully simulated under laboratory conditions. In this paper, concrete strength grade was predicted by using the floating centroids method neural network classifier, which removes the fixed-centroid constraint and increases the possibility of finding an optimal neural network. Experimental results show that concrete strength prediction performance is improved by employing the floating centroids method. (en)
Title
  • Prediction of Concrete Strength using Floating Centroids Method
  • Prediction of Concrete Strength using Floating Centroids Method (en)
skos:prefLabel
  • Prediction of Concrete Strength using Floating Centroids Method
  • Prediction of Concrete Strength using Floating Centroids Method (en)
skos:notation
  • RIV/61989100:27740/13:86089354!RIV14-MSM-27740___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
  • Abraham Padath, Ajith
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 98578
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27740/13:86089354
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Neural network; Floating centroids method; Concrete strength (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [A4473F0D3F50]
http://linked.open...v/mistoKonaniAkce
  • Manchester
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Abraham Padath, Ajith
  • Yang, Bo
  • Wang, Lin
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/SMC.2013.173
http://purl.org/ne...btex#hasPublisher
  • IEEE
https://schema.org/isbn
  • 978-0-7695-5154-8
http://localhost/t...ganizacniJednotka
  • 27740
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software