|
Title
|
OPTIMIZATION OF MIXTURE PROPORTIONS OF ROLLER COMPACTED CONCRETE BASED ON NEURAL NETWORK MODELING
|
|
Type
|
JournalPaper
|
|
Keywords
|
ROLLER COMPACTED CONCRETE, COMPRESSIVE STRENGTH, MINIMUM RCC PASTES, MIXTURE DESIGN, NEURAL NETWORK
|
|
Abstract
|
Roller compacted concrete (RCC) is a no slump concrete which is widely used in the construction of dams and road pavements. Mixture design of such concretes containing minimum cement pastes is necessary to control the thermal problems in mass concretes. With an accurate model for prediction of RCC compressive strength, it is possible to optimize RCC mixture design. Nevertheless, RCC is a highly complex material that modeling its behavior is a difficult task and needs nonlinear modeling methods. In the present study, attempt was made to propose a neural network model for prediction of compressive strength of roller compacted concretes. Necessary data obtained from the mixture designs of various laboratory and field test results. Neural network (NN) has strong capability of modeling complex, multivariable and nonlinear problems such as RCC mixture design. In the concrete mixtures, minimum cement pastes are used to design concretes for the required compressive strength. With the proposed NN model in this investigation, it is possible to estimate the compressive strength of roller compacted concretes as the output of the model.
|
|
Researchers
|
HamidReza Rahmani (Fourth Researcher), Mohammad Esmaeilnia omran (Third Researcher), Khosrow Sadeghi Mrzaleh (Second Researcher), Ali akbar Ramazanpour (First Researcher)
|