Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predication of GRT Fiber-Rubberized Haydite Concrete Bend Strength Based on Multiple Regression 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 bend strength. Through the analysis of the test data, using multiple regression analysis established the GRT fiber—rubberized haydite concrete 7d and 28d bend strength regression formulas. By means of BP neural network theory combine MATLAB programme established GRT fiber—rubberized haydite concrete 7d and 28d bend 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 of 28d’s and 28d’s BP neural network model are availabled. But because of the water and cement which in the GRT fiber—rubberized haydite concrete can not hydration reaction sufficiently during the 7d’s, so the multiple regression equations of 7d’s is unavailabled.
Predication of GRT Fiber-Rubberized Haydite Concrete Bend Strength Based on Multiple Regression 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 bend strength. Through the analysis of the test data, using multiple regression analysis established the GRT fiber—rubberized haydite concrete 7d and 28d bend strength regression formulas. By means of BP neural network theory combine MATLAB programme established GRT fiber—rubberized haydite concrete 7d and 28d bend 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 of 28d’s and 28d’s BP neural network model are availabled. But because of the water and cement which in the GRT fiber—rubberized haydite concrete can not hydration reaction sufficiently during the 7d’s, so the multiple regression equations of 7d’s is unavailabled.
Predication of GRT Fiber-Rubberized Haydite Concrete Bend Strength Based on Multiple Regression Analysis and BP Neural Network
Xia, Bing-Hua (Autor:in) / Liu, Yuan-Cai (Autor:in) / Sun, Wei-Wei (Autor:in)
2012
7 Seiten
Aufsatz (Konferenz)
Englisch
| British Library Conference Proceedings | 2012
Elastic Modulus Calculation of GRT Fiber-Rubberized Haydite Concrete
| Tema Archiv | 2012
Elastic Modulus Calculation of GRT Fiber-Rubberized Haydite Concrete
| British Library Conference Proceedings | 2012
Disclose nature of haydite concrete
| Engineering Index Backfile | 1931