A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Predicition of GRT Fiber-Rubberized Haydite Concrete Compressive Strength Based on Multiple Regreeion Analysis and BP Neural Network
Experiment with intensity level for the LC30 ceramsite concrete as the research object, changing the content of cement, GRT fiber, rubber powder by the orthogonal test to configure GRT fiber—rubberized haydite concrete samples, maintenance samples 7d and 28d in standard conditions and respectively testing their standard compressive strength. Through the analysis of the test data, using multiple regression analysis established the GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength regression formulas. By means of BP neural network theory combine MATLAB programme established GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength neural network model. Finally using 3 groups new test data to compare the value of multiple regression equations and BP neural network’s predicted value. The results indicate that the multiple regression equations and BP neural network model are availabled.
Predicition of GRT Fiber-Rubberized Haydite Concrete Compressive Strength Based on Multiple Regreeion Analysis and BP Neural Network
Experiment with intensity level for the LC30 ceramsite concrete as the research object, changing the content of cement, GRT fiber, rubber powder by the orthogonal test to configure GRT fiber—rubberized haydite concrete samples, maintenance samples 7d and 28d in standard conditions and respectively testing their standard compressive strength. Through the analysis of the test data, using multiple regression analysis established the GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength regression formulas. By means of BP neural network theory combine MATLAB programme established GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength neural network model. Finally using 3 groups new test data to compare the value of multiple regression equations and BP neural network’s predicted value. The results indicate that the multiple regression equations and BP neural network model are availabled.
Predicition of GRT Fiber-Rubberized Haydite Concrete Compressive Strength Based on Multiple Regreeion Analysis and BP Neural Network
Xia, Bing-Hua (author) / Liu, Yuan-Cai (author) / Zhu, De-Bin (author)
2012
6 Seiten
Conference paper
English
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