Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models

By J. Sobhani, M. Najimi, A. Pourkhorshidi, T. Parhizkar. 2010

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Machine learning in concrete strength simulations: Multi-nation data analytics (2014, background): “An important research problem in materials science is predicting the mechanical properties of construction materials [1].”
Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks (2013, background): “Some studies have been published, showing that ANN can model complex and nonlinear relationships between parameters affecting the compressive strength of concrete [10–17], the compressive strength of high strength concrete [18], the compressive strength”
Optimum mix design of geopolymer pastes and concretes cured in ambient condition based on compressive strength, setting time and workability (2019, methodology): “15 Numerous studies have used multivariable regression models to predict the properties of paste, mortar 356 and concrete with excellent results [14, 37-40].”
Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed (2013, methodology): “Detailed description of ANFIS theory is discussed more in reviews (Azamathulla et al., 2009; Jang and Sun, 1995; Sobhani et al., 2010; Takagi and Sugeno, 1985; Tay and Zhang, 1999).”