A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Application of Neural Network to predict the workability Parameters of Self-Compacting Concrete
The paper presents a new application of classical multi-layer perceptron neural network to approximate the parameters of fresh self-compacting concrete. The approximation is needed to determine the working parameters at construction site and can be used to estimate the components used at mixing station to achieve the desired concrete quality. A number of real field tests were conducted and six basic parameters were measured for each test. The numerical results showed the high accuracy of proposed solution.
Application of Neural Network to predict the workability Parameters of Self-Compacting Concrete
The paper presents a new application of classical multi-layer perceptron neural network to approximate the parameters of fresh self-compacting concrete. The approximation is needed to determine the working parameters at construction site and can be used to estimate the components used at mixing station to achieve the desired concrete quality. A number of real field tests were conducted and six basic parameters were measured for each test. The numerical results showed the high accuracy of proposed solution.
Application of Neural Network to predict the workability Parameters of Self-Compacting Concrete
Lecture Notes in Civil Engineering
Ha-Minh, Cuong (editor) / Dao, Dong Van (editor) / Benboudjema, Farid (editor) / Derrible, Sybil (editor) / Huynh, Dat Vu Khoa (editor) / Tang, Anh Minh (editor) / Nguyen, Cuong H. (author) / Tran, Linh H. (author) / Ho, Khoa N. (author)
2019-10-11
6 pages
Article/Chapter (Book)
Electronic Resource
English
Self-compacting Concrete , Nonlinear Approximation , Feed forward Neural Network , Multi-layer Perceptron Engineering , Geoengineering, Foundations, Hydraulics , Sustainable Development , Landscape/Regional and Urban Planning , Structural Materials , Building Construction and Design , Transportation Technology and Traffic Engineering
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