Using an Artificial Neural Network to Predict Mix Compositions of Steel Fiber-Reinforced Concrete

2014 ◽  
Vol 40 (2) ◽  
pp. 407-419 ◽  
Author(s):  
Merve Açikgenç ◽  
Mustafa Ulaş ◽  
Kürşat Esat Alyamaç
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Steel Fiber ◽  
Fiber Reinforced Concrete ◽  
Fiber Reinforced ◽  
Steel Fiber Reinforced Concrete ◽  
Artificial Neural

scholarly journals ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

Fibers ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 88 ◽  
Author(s):  
Miguel Abambres ◽  
Eva O.L. Lantsoght
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Coefficient Of Variation ◽  
Steel Fiber ◽  
Shear Capacity ◽  
Fiber Reinforced Concrete ◽  
Shear Reinforcement ◽  
Steel Fiber Reinforced Concrete ◽  
Proposed Model

Comparing experimental results of the shear capacity of steel fiber-reinforced concrete (SFRC) beams without stirrups to the capacity predicted using current design equations and other available formulations shows that predicting the shear capacity of SFRC beams without mild steel shear reinforcement is still difficult. The reason for this difficulty is the complex mechanics of the problem, where the steel fibers affect the different shear-carrying mechanisms. Since this problem is still not fully understood, we propose the use of artificial intelligence (AI) to derive an expression based on the available experimental data. We used a database of 430 datapoints obtained from the literature. The outcome is an artificial neural network-based expression to predict the shear capacity of SFRC beams without shear reinforcement. For this purpose, many thousands of artificial neural network (ANN) models were generated, based on 475 distinct combinations of 15 typical ANN features. The proposed “optimal” model results in maximum and mean relative errors of 0.0% for the 430 datapoints. The proposed model results in a better prediction (mean Vtest/VANN = 1.00 with a coefficient of variation 1 × 10−15) as compared to the existing code expressions and other available empirical expressions, with the model by Kwak et al. giving a mean value of Vtest/Vpred = 1.01 and a coefficient of variation of 27%. Until mechanics-based models are available for predicting the shear capacity of SFRC beams without shear reinforcement, the proposed model thus offers an attractive solution for estimating the shear capacity of SFRC beams without shear reinforcement. With this approach, designers who may be reluctant to use SFRC because of the large uncertainties and poor predictions of experiments, may feel more confident using the material for structural design.

Acoustic emission by steel fiber reinforced concrete under tensile damage

2017 ◽  
Vol 59 (7-8) ◽  
pp. 653-660 ◽  
Author(s):  
Wang Yan ◽  
Ge Lu ◽  
Chen Shi Jie ◽  
Zhou Li ◽  
Zhang Ting Ting
Keyword(s):  
Acoustic Emission ◽  
Reinforced Concrete ◽  
Steel Fiber ◽  
Fiber Reinforced Concrete ◽  
Fiber Reinforced ◽  
Steel Fiber Reinforced Concrete ◽  
Tensile Damage
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