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Compressive Strength Modelling Using Artificial Neural Network (ANN) for Concrete with Waste Foundry Sand as Partial Fine Aggregate

Author(s): R. Muthukumaran(1), M. Nithya (2), A. K. Priya (2), V. Aparna (2) and D.Vivek(2)
Paper category: Proceedings
Book title: Proceedings of the 71st RILEM Annual Week & ICACMS 2017,Chennai,India, 3rd -8th September 2017
Editor(s): Manu Santhanam
Ravindra Gettu
Radhakrishna G. Pillai
Sunitha K. Nayar
ISBN: 978-2-35158-196-4
ISBN: 978-2-35158-190-2 (Set)
e-ISBN: 978-2-35158-191-9:
Publisher: Published by RILEM Publications S.A.R.L.
Publication year: 2017
Pages: 162-167
Total Pages: 6
Language : English

Abstract: Concrete remains as the most universally used construction material even after the
introduction of new materials in the construction industry. The concrete is a composite
substance composed of cement, sand, gravel and water. The continuous depletion of river
sand for use as fine aggregate in manufacture of concrete is one of the biggest man – made
disasters, which affects the sustainable development. Thus, it is essential to find an alternative
for fine aggregate to protect our environment from river sand mining. This paper aims to
proportionate the use of Waste Foundry Sand (WFS) for partial replacement of fine aggregate
in concrete by developing an Artificial Neural Network (ANN) model using MATLAB and
to determine the compressive strength of concrete. There are ten input parameters for ANN
model, which includes mass of cement, coarse aggregate, sand, water, foundry sand, water
cement ratio, specific gravity and fineness modulus of foundry sand, super plasticizer and
others and, an output parameter, which is compressive strength of concrete at 28 days. The
comparison of results of ANN model with that of experimental results reveals that ANN
prove to be a potential tool for predicting the compressive strength of concrete with foundry
sand for partial replacement of fine aggregate in concrete.

Online publication : 2017
Publication type : full_text
Public price (Euros) : 0.00

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