Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models
| Autor: | Jafar Sobhani, T. Parhizkar, A.R. Pourkhorshidi, Meysam Najimi |
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| Rok vydání: | 2010 |
| Předmět: |
Engineering
Adaptive neuro fuzzy inference system Artificial neural network business.industry Regression analysis Building and Construction Structural engineering Compression (physics) Strength of materials Regression Slump Compressive strength General Materials Science Geotechnical engineering business Civil and Structural Engineering |
| Zdroj: | Construction and Building Materials. 24:709-718 |
| ISSN: | 0950-0618 |
| DOI: | 10.1016/j.conbuildmat.2009.10.037 |
| Popis: | No-slump concrete (NSC) is defined as concrete having either very low or zero slump that traditionally used for prefabrication purposes. The sensitivity of NSC to its constituents, mixture proportion, compaction, etc., enforce some difficulties in the prediction of the compressive strength. In this paper, by considering concrete constituents as input variables, several regression, neural networks (NNT) and ANFIS models are constructed, trained and tested to predict the 28-days compressive strength of no-slump concrete (28-CSNSC). Comparing the results indicate that NNT and ANFIS models are more feasible in predicting the 28-CSNSC than the proposed traditional regression models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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