Fiber-reinforced concrete pavement design and material requirements

2009 ◽  
Author(s):  
J Roesler ◽  
A Bordelon
Keyword(s):  
Reinforced Concrete ◽  
Concrete Pavement ◽  
Pavement Design ◽  
Fiber Reinforced Concrete ◽  
Fiber Reinforced ◽  
Material Requirements

The Application of Fiber Reinforced Concrete in Cement Concrete Pavement

2013 ◽  
Vol 634-638 ◽  
pp. 2094-2097 ◽  
Author(s):  
Tao Zhang ◽  
Yun Yun Xu ◽  
Zhen Rong Lin
Keyword(s):  
Reinforced Concrete ◽  
Building Materials ◽  
Impact Resistance ◽  
Research Field ◽  
Concrete Pavement ◽  
Fiber Reinforced Concrete ◽  
Fiber Reinforced ◽  
Cement Concrete ◽  
Cement Concrete Pavement ◽  
The Status

Concrete is the most widely used building material in the world. In terms of traffic and bridge construction, concrete is also the most widely used building materials. The cement concrete of the inherent advantages of having a high compressive strength while also has easy to produce cracks, poor impact resistance inherent weaknesses. These weaknesses limit the application of cement concrete. In order to improve the comprehensive quality of the concrete, to further improve the scope of application of cement concrete pavement, the main problems of cement concrete pavement is analyzed. Then the progress of the current hot research field of fiber reinforced concrete is analyzed comprehensively. Finally, on the basis of summing up the status quo, the next step is to study key issues is presented.

scholarly journals PREDICTION OF BASALT FIBER REINFORCED CONCRETE PAVEMENT BENDING STRENGTH VALUES

2020 ◽  
Vol 4 (11) ◽  
pp. 19-24
Author(s):  
Hidayet BAYRAKTAR ◽  
Ayhan SAMANDAR ◽  
Suat SARIDEMİR
Keyword(s):  
Reinforced Concrete ◽  
Bending Strength ◽  
Basalt Fiber ◽  
Concrete Pavement ◽  
Fiber Reinforced Concrete ◽  
Data Sets ◽  
Concrete Pavements ◽  
Fiber Reinforced ◽  
Prediction Ability ◽  
Artificial Neural Network Ann

This paper proposes the potential of artificial neural network (ANN) system for estimating the bending strength values of the basalt fiber reinforced concrete pavements. Three main influential parameters; namely basalt fiber ratio, density and slump value of the fresh concrete were selected as input data. The model was trained, tested using 400 data sets which were the results of on-site experiment tests. ANN system results were also compared with the experimental test results. The research results showed that the proposed models have strong and accurate prediction ability for the basalt reinforced concrete pavement composites.

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