Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network-Reference-Cited by-同舟云学术

Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network

Published:2012-02 Issue:1 Volume:27 Page:404-414
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Uysal Mucteba,Tanyildizi Harun

Publisher

Elsevier BV

Subject

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

Reference51 articles.

1. Experimental behaviour of high performance concrete-filled steel tubular columns;Yua;Thin Wall Struct,2008

2. Properties of self-compacting concrete containing class F fly ash;Siddique;J Mater Des,2011

3. Effect of Metakaolin’s finesses and content in self-consolidating concrete;Melo;Constr Build Mater,2010

4. Self-compacting concrete with different levels of pulverized fuel ash;Liu;Constr Build Mater,2010

5. Effect of slag on the rheology of fresh self-compacted concrete;Boukendakdji;Constr Build Mater,2009

Cited by 173 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimizing pervious concrete with machine learning: Predicting permeability and compressive strength using artificial neural networks;Construction and Building Materials;2024-09

2. Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning;Results in Engineering;2024-09

3. Neural network - Game theory coupled approach for predicting flexural performance of fibre-reinforced concrete;Journal of Building Engineering;2024-06

4. Predicting the Mix Proportions of Concrete Containing Calcined Clay Using Hybridized CNN and XGB and Employing the Shapley Method for Sensitivity Analysis;Arabian Journal for Science and Engineering;2024-05-10

5. Application of Relationship model for the Measurement While Drilling Data to Predict Rock Uniaxial Compressive Strength for Tunneling;Rock Mechanics and Rock Engineering;2024-05-03