Prognostication of Concrete Mix Proportion by ANN Approach


Article Preview

An exhaustive literature survey shows, that a very little effort has been done towards Artificial Neural Network (ANN) approach in the area of concrete technology [1, 2, 3]. In the present investigation, development of ANN approach for prognostication of concrete mix proportion in lieu of conventional laboratory approach. The traditional lab approach attracts some drawbacks such as lot of manual involvement, time consuming, chances of creeping of human errors, uncertain prediction and always invasive in nature. Hence to reduce above said drawbacks, this study is undertaken to develop a ANN between concrete mix ingredient properties namely maximum size of aggregate, degree of quality control, degree of workability, type of exposure, characteristic compressive strength required in the field at 28 days and concrete mix proportion. Prognostication of concrete mix proportion is essential for all structural works. The present work deals with collection of huge input data base from literatures, ANN’s training and its testing are adopted to fix the appropriate weighted matrix (Illustrated in Fig [1]) which in turn Prognosticates the appropriate concrete mix proportion. Indian standard code (IS 10262:1982) procedure is also adopted to compare the concrete mix proportions of same samples. The Prognosticated concrete mix proportion from ANN approach yielded very high accuracy results (As shown in fig [2]) compared with IS code method. To account for larger sample data the results of this work will contribute for the prognostication of concrete mix proportions up to a certain degree of level, which will assist a structural engineer in estimation of concrete mix proportion, with minimum effort and non- invasive technique.



Advanced Materials Research (Volumes 261-263)

Edited by:

Jingying Zhao






K. Gowda and G.L.E. Prasad, "Prognostication of Concrete Mix Proportion by ANN Approach", Advanced Materials Research, Vols. 261-263, pp. 426-430, 2011

Online since:

May 2011




In order to see related information, you need to Login.

In order to see related information, you need to Login.