Title of article :
MODELING FLEXURAL STRENGTH OF EPS LIGHTWEIGHT CONCRETE USING REGRESSION, NEURAL NETWORK and ANFIS
Author/Authors :
Sobhani, J Department of Concrete Technology - Road, Housing , Ejtemaei, M School of Chemical Engineering - Iran University of Science & Technology, Tehran , Sadrmomtazi, A Faculty of Engineering - University of Guilan, Rasht , Mirgozar, M.A Department of Civil Engineering - Fouman and Shaft Branch
Abstract :
Lightweight concrete (LWC) is a kind of concrete that made of lightweight aggregates or
gas bubbles. These aggregates could be natural or artificial, and expanded polystyrene (EPS)
lightweight concrete is the most interesting lightweight concrete and has good mechanical
properties. Bulk density of this kind of concrete is between 300-2000 kg/m3. In this paper
flexural strength of EPS is modeled using four regression models, nine neural network
models and four adaptive Network-based Fuzzy Interface System model (ANFIS). Among
these models, ANFIS model with Bell-shaped membership function has the best results and
can predict the flexural strength of EPS lightweight concrete more accurately.
Keywords :
EPS concrete , silica fume , flexural strength , modeling , regression , neural network , ANFIS
Journal title :
Astroparticle Physics
