scholarly journals Incremental displacement collocation method for the evaluation of tension softening curve of mortar

2012 ◽  
Vol 88 ◽  
pp. 49-62 ◽  
Author(s):  
R.K.L. Su ◽  
H.H.N. Chen ◽  
A.K.H. Kwan
Keyword(s):  
Collocation Method ◽  
Softening Curve ◽  
Tension Softening

scholarly journals Determination of the tension softening curve of nuclear graphites using the incremental displacement collocation method

Carbon ◽  
2013 ◽  
Vol 57 ◽  
pp. 65-78 ◽  
Author(s):  
R.K.L. Su ◽  
H.H. Chen ◽  
S.L. Fok ◽  
H. Li ◽  
G. Singh ◽  
...  
Keyword(s):  
Collocation Method ◽  
Softening Curve ◽  
Tension Softening

Prediction of Tension Softening Curve in Concrete Using Artificial Neural Networks

2008 ◽  
Vol 41-42 ◽  
pp. 277-282 ◽  
Author(s):  
Dariusz Alterman ◽  
Hiroshi Akita
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Uniaxial Tension ◽  
High Performance ◽  
High Performance Concrete ◽  
Softening Curve ◽  
Fracture Parameters ◽  
Tension Softening ◽  
Artificial Neural ◽  
Institute Of Technology

Knowledge of the tension softening process of concrete is essential to understand fracture mechanism, further to analyze fracture behaviour, and further to evaluate properties of concrete. For the last eight years, many different tests on uniaxial tension with elimination of secondary flexure were performed in Tohoku Institute of Technology. The paper is dedicated to predict tension softening curve of concrete by using artificial neural networks (ANNs) based on experimental data of five different mixtures of concrete (including High Performance Concrete). It is an advantage to predict a proper tension softening curve without performing uniaxial tension tests. Several artificial neural networks with different architectures (with various hidden neurons and layers) were studied using software - Statistica Neural Network. In order to evaluate the prediction accuracy, tension softening curve and other fracture parameters were predicted for each mix from the other four mixes and compared with the omitted data of the relevant mix. High accuracy was obtained in the all predicted tension softening curves and the fracture parameters were also well predicted.

Prediction of the Tension Softening Curve of Dam Concrete Based on BP Neural Network

2011 ◽  
Vol 311-313 ◽  
pp. 1935-1940
Author(s):  
Zhi Gang Zhao ◽  
Zhi Hua Yang ◽  
Zhi Fang Zhao ◽  
Min Min Zhu
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Direct Tension ◽  
Softening Curve ◽  
Tension Tests ◽  
Tension Softening ◽  
Component Material ◽  
Dam Concrete

This paper predicted the tension softening curves of dam concrete by employing the BP neural networks based on the experimental data of direct tension tests of dam concrete. This approach can predict the tension softening curves for the same component material but different mixtures dam concrete without performing the direct tension tests which are complex and expensive.

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