Evaluation of interface shear transfer strength of steel fiber‐reinforced concrete based on artificial neural network and regression method

2022 ◽  
Author(s):  
Bai Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Steel Fiber ◽  
Regression Method ◽  
Fiber Reinforced Concrete ◽  
Steel Fiber Reinforced Concrete ◽  
Interface Shear ◽  
Shear Transfer ◽  
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.

scholarly journals Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

2019 ◽  
Author(s):  
Temitope F. Awolusi ◽  
Oluwaseyi L. Oke ◽  
Olufunke O. Akinkurolere ◽  
Olumoyewa Atoyebi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Steel Fiber ◽  
Fiber Reinforced Concrete ◽  
Comparative Approach ◽  
Split Tensile Strength ◽  
Training Approach ◽  
Artificial Neural

The study presents a comparative approach between response surface methodology (RSM) and hybridized, genetic algorithm artificial neural network (GA-ANN) in predicting the water absorption, compressive strength, flexural strength split tensile strength and slump for steel fiber reinforced concrete. The effect of process variables such as aspect ratio, water cement ratio and cement content were investigated using the central composite design of response surface methodology.  This same experimental design was used in training the hybrid-training approach of artificial neural network. The predicting ability of both methodologies were compared using the  root mean sqaured error (RMSE), mean absolute error (MAE), model predictive error (MPE) and absolute average deviation (AAD). The RSM model was found more accurate in prediction compared to hybrid GA-ANN.

Sign in / Sign up

Export Citation Format

Share Document