Design of concrete mixes by systematic steps and ANN
2012 (English)In: Journal of Advanced Science and Engineering Research, ISSN 2231-8844, Vol. 2, no 4, 232-251 p.Article in journal (Refereed) Published
The current research caters for the possibility of arriving at a system for designing concrete mixeseasily using available materials locally by specified wide ranges of pre-requisites of three mainprescribed properties to cover a good variety of practical mixes, which are water, water-cement ratioand total aggregate-cement ratio. Using these three properties, a tri-linear form was constructed bygraphical technique manner based on absolute volume approach. This approach defines as asummation of absolute volume for each of these three materials individually water, cement andaggregate should be equal to the absolute volume of whole concrete mixture based on thesealtogether. A quad-form area which includes a wide range of mixes can be formed from thisrepresentation. This area should achieve all the prescribed properties aforementioned. Artificial neuralnetwork concept used in this study also to build easily and quickly system which can be translatedinto Excel sheet. This system predict proportions of concrete mixture and the compressive strengthusing the results designed by the quad-form area method in addition to the data from literature around500 mixes based on local materials used in Iraq. Six input parameters (water to cement ratio, theslump, % of fine to total aggregate content, maximum aggregate size, fineness modulus of fineaggregate and the compressive strength) were used in this system to get the outputs. In addition, nineinput parameters ((water, cement, sand and gravel contents) and the properties of the mix (Finenessmodulus, W/C ratio, the slump, % of fine to total aggregate and the M.A.S)) were used as basis ofcompressive strength model. The algorithm of this system aimed to reduce the high number of trailmixes error as well as saving the labors, cost and time. Results indicated that the concrete mix designand the compressive strength model can be predicted accurately by using graphical perspective andthe ANN approach.
Place, publisher, year, edition, pages
2012. Vol. 2, no 4, 232-251 p.
Concrete, Compressive strength model, Artificial Neural Network, Quick method, Quad-form area method, Graphical solution, Civil engineering and architecture - Building engineering
Samhällsbyggnadsteknik och arkitektur - Byggnadsteknik
Research subject Soil Mechanics; Structural Engineering
IdentifiersURN: urn:nbn:se:ltu:diva-13880Local ID: d30927b0-95c3-4544-963d-19658c227c91OAI: oai:DiVA.org:ltu-13880DiVA: diva2:986833
Validerad; 2012; 20121208 (mohhat)2016-09-292016-09-29Bibliographically approved