A comparison of machine learning methods for predicting the compressive strength of field-placed concrete-Reference-Cited by-同舟云学术

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A comparison of machine learning methods for predicting the compressive strength of field-placed concrete

Published:2019-12 Issue: Volume:228 Page:116661
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

DeRousseau M.A.,Laftchiev E.,Kasprzyk J.R.,Rajagopalan B.,Srubar W.V.

Funder

National Science Foundation

Publisher

Elsevier BV

Subject

General Materials Science,Building and Construction,Civil and Structural Engineering

Reference47 articles.

1. ACI Committee 318, Building code requirements for reinforced concrete, American Concrete Institute, ACI 318, 2014.

2. Neural networks for predicting compressive strength of structural light weight concrete;Alshihri;Constr. Build. Mater.,2009

3. Predicting the compressive strength and slump of high strength concrete using neural network;Oztas;Constr. Build. Mater.,2006

4. Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network;Bilim;Adv. Eng. Softw.,2009

5. Prediction of compressive strength of concrete by neural networks;Ni;Cem. Concr. Res.,2000

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