Application of Neural Networks for Predicting the Workability of Self-Compacting Concrete

References (32)

1. 1 Modeling of strength of high-performance concrete using artificial neural networks [DOI]
2. 2 Knowledge‐Based Modeling of Material Behavior with Neural Networks [DOI]
3. 3 Influence of limestone powder used as filler in SCC on hydration and microstructure of cement pastes [DOI]
4. 4 Effect of water/cement ratio on the fresh and hardened properties of self-compacting concrete [DOI]
5. 5 A review of the hardened mechanical properties of self-compacting concrete [DOI]
6. 6 The effect of chemical admixtures and mineral additives on the properties of self-compacting mortars [DOI]
7. 7 Modeling slump flow of concrete using second-order regressions and artificial neural networks [DOI]
8. 8 Effect of different supplementary cementitious materials on mechanical properties of high performance concrete [DOI]
9. 9 Self-compacting concrete. Development, present use and future
10. 10 HPC Strength Prediction Using Artificial Neural Network [DOI]
11. 11 Effect of chemical structure of polycarboxylate-based superplasticizers on workability retention of self-compacting concrete [DOI]
12. 12 Computational modeling of concrete flow: General overview [DOI]
13. 13 The use of quarry dust for SCC applications [DOI]
14. 14 A new method for the mix design of medium strength flowing concrete with low cement content [DOI]
15. 15 A concrete mix proportion design algorithm based on artificial neural networks [DOI]
16. 16 An experimental study on the workability of self-compacting lightweight concrete [DOI]
17. 17 Modeling Concrete Strength with Augment-Neuron Networks [DOI]
18. 18 Comparison of Field-Oriented Test Methods to Assess Dynamic Stability of Self-Consolidating Concrete [DOI]
19. 19 Rapid testing method for segregation resistance of self-compacting concrete [DOI]
20. 20 Correlation between L-box test and rheological parameters of a homogeneous yield stress fluid [DOI]
21. 21 High strength self-compacting concrete Original solutions associating organic and inorganic admixtures [DOI]
22. 22 Using neural networks to predict workability of concrete incorporating metakaolin and fly ash [DOI]
23. 23 The investigation on the workability of fibre cocktail reinforced self-compacting high performance concrete [DOI]
24. 24 Evaluation of Static Stability of Self-Consolidating Concrete [DOI]
25. 25 Predicting Performance of Self-Compacting Concrete Mixtures Using Artificial Neural Networks [DOI]
26. 26 Exploring Concrete Slump Model Using Artificial Neural Networks [DOI]
27. 27 The properties of cementitious materials superplasticized with two superplasticizers based on aminosulfonate–phenol–formaldehyde [DOI]
28. 28 Computational Simulation of Composite Ply Micromechanics Using Artificial Neural Networks [DOI]
29. 29 Learning Internal Representations by Error Propagation [DOI]
30. 30 Concrete: Structure, properties and materials [DOI]
31. 31 Tensile strength and bonding characteristics of self-compacting concrete [DOI]
32. 32 Book review: Neural Network and Fuzzy Logic Applications in C/C++ by Stephen T. Welstead (John Wiley & Sons 1994) [DOI]

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