scholarly journals Evaluating the Effects of Steel Fibers on Mechanical Properties of Ultra-High Performance Concrete Using Artificial Neural Networks

2018 ◽  
Vol 8 (7) ◽  
pp. 1120 ◽  
Author(s):  
Dechao Qu ◽  
Xiaoping Cai ◽  
Wei Chang
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Performance ◽  
High Performance Concrete ◽  
Steel Fibers ◽  
Ultra High Performance Concrete ◽  
Artificial Neural

Experimental Study of Strengthening and Toughening for Recycled Steel Fiber Reinforced Ultra-High Performance Concrete

2014 ◽  
Vol 629-630 ◽  
pp. 104-111 ◽  
Author(s):  
Gai Fei Peng ◽  
Xu Jing Niu ◽  
Qian Qian Long
Keyword(s):  
Mechanical Properties ◽  
Fracture Energy ◽  
High Performance ◽  
Steel Fiber ◽  
High Performance Concrete ◽  
Steel Fibers ◽  
Fiber Reinforced ◽  
Ultra High Performance Concrete ◽  
Tensile Splitting Strength ◽  
Recycled Steel

This paper presents an experimental investigation on mechanical properties (including compressive strength, tensile splitting strength and fracture energy) of ultra-high performance concrete (UHPC) with recycled steel fiber, compared with none fiber and industrial steel fiber reinforced UHPC. Moreover, the microscopic observation of fracture energy was carried out. All specimens were prepared at 0.18 water /binder (W/B) ratio and the dosage of steel fiber was controlled at 60 kg/m3. The results indicate that recycled steel fiber has a significant effect on enhancing strength and toughness of UHPC. And owing to the crimped shape, higher tensile strength (1800-2000 MPa) and appropriate diameter (1 mm) of recycled steel fiber, the steel fibers of UHPRSFRC will not immediately be pulled off and necking phenomenon is distinct.

scholarly journals Results of Application of Artificial Neural Networks in Predicting Geo-Mechanical Properties of Stabilised Clays—A Review

Geotechnics ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 147-171
Author(s):  
Jeremiah J. Jeremiah ◽  
Samuel J. Abbey ◽  
Colin A. Booth ◽  
Anil Kashyap
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
High Performance ◽  
Cementitious Materials ◽  
Convergence Time ◽  
Ann Model ◽  
Marquardt Algorithm ◽  
Artificial Neural

This study presents a literature review on the use of artificial neural networks in the prediction of geo-mechanical properties of stabilised clays. In this paper, the application of ANNs in a geotechnical analysis of clay stabilised with cement, lime, geopolymers and by-product cementitious materials has been evaluated. The chemical treatment of expansive clays will involve the development of optimum binder mix proportions or the improvement of a specific soil property using additives. These procedures often generate large data requiring regression analysis in order to correlate experimental data and model the performance of the soil in the field. These analyses involve large datasets and tedious mathematical procedures to correlate the variables and develop required models using traditional regression analysis. The findings from this study show that ANNs are becoming well known in dealing with the problem of mathematical modelling involving nonlinear functions due to their robust data analysis and correlation capabilities and have been successfully applied to the stabilisation of clays with high performance. The study also shows that the supervised ANN model is well adapted to dealing with stabilisation of clays with high performance as indicated by high R2 and low MAE, RMSE and MSE values. The Levenberg–Marquardt algorithm is effective in shortening the convergence time during model training.

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.

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