Prediction of the compressive strength of volcanic tuff mineral additive concrete using artificial neural network
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s12517-021-08637-4.pdf
Reference46 articles.
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2. Seher A (2020a) Estimation of hanging and removal times in eloxal with artificial neural networks. Emerging Materials Research 9(2):366–374. https://doi.org/10.1680/jemmr.19.00191
3. Seher Arslankaya (2020) Estimating the effects of heat treatment on aluminum alloy with artificial neural networks Emerging. Materials Research 9(2):540–549. https://doi.org/10.1680/jemmr.20.00059
4. ASTM C 125 (1994) Standard terminology relating to concrete and concrete aggregates. Annual Book of ASTM Standards
5. ASTM C 618 (1994) Standard specification for coal fly ash and raw or calcined natural pozzolana for use as a mineral admixture in Portland cement concrete. Annual Book of ASTM Standards
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1. Optimization of Tuff Stones Content in Lightweight Concrete Using Artificial Neural Networks;Civil Engineering Journal;2023-11-01
2. The effect of adding zeolite and silica fume on properties of hardened ordinary and high strength concretes with cement grades 32.5, 42.5, and 52.5;Arabian Journal of Geosciences;2023-05-15
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