Prediction of the ultimate strength of reinforced concrete beams FRP-strengthened in shear using neural networks

The nominal strength of slab-column joints made of highstrength concrete (HSC) columns and normal strength concrete (NSC) slabs is of great importance in structural design and construction of concrete buildings. This topic has been intensively studied during the last decades. Different types of column-slab joints have been investigated experimentally providing a basis for developing design provisions. However, available data does not cover all classes of concretes, reinforcements, and possible loading cases for the proper calculation of joint stresses necessary for design purposes. New numerical methods based on modern software seem to be effective and may allow reliable prediction of column-slab joint strength. The current research is focused on analysis of available experimental data on different slab-to-column joints with the aim of predicting the nominal strength of slabcolumn joint. Neural networks technique is proposed herein using MATLAB routines developed to analyze available experimental data. The obtained results allow prediction of the effective strength of column-slab joints with accuracy and good correlation coefficients when compared to regression based models. The proposed method enables the user to predict the effective design of column-slab joints without the need for conservative safety coefficients generally promoted and used by most construction codes.

Section: Aims and Scope Of The Researchmentioning

confidence: 99%

Section: Neural Network Modelmentioning

confidence: 99%

Estimation of RC slab-column joints effective strength using neural networks

Shah

Ribakov

2011

“…Artificial neural networks (ANN)were previously used to predicting the concrete properties (Flood et al, 2001;Pannir selvam et al, 2008;Perera et al, 2010;Yang et al, 2008;Guang and Zong, 2000;Goh ATC, 1995;Sanad and Saka, 2001;Jamal et al, 2007;Arslan, 2009;Arslan et al, 2007).From a literature study it could be find that there is no investigation on the combined effects of fibers and nano-silica on the mechanical Properties of SCC using ANN.…”

Section: Introductionmentioning

confidence: 99%

Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network

Tavakoli

Omran

Shiade

et al. 2014

In this research, the combined effect of nano-silica particles and three fiber types (steel, polypropylene and glass) on the mechanical properties (compressive, tensile and flexural strength) of reinforced self-compacting concrete(SCC) is evaluated. For this purpose, 70 mixtures in A, B, C, D, E, F and G series representing 0, 1, 2, 3, 4, 5 and 6 percent of nano-silica particles in replacing cement content are cast. Each series involves three different fiber types and content; 0.2, 0.3 and 0.5% volume for steel fiber, 0.1, 0.15 and 0.2% of volume for polypropylene fiber and finally 0.15, 0.2 and 0.3% of volume for glass fiber. The results show that the simultaneous usage of an optimum percentage of fiber and nano-silica particles will improve the mechanical properties of SCC. Moreover, the obtained results from the experimental data are used to train a multi-layer perceptron (MLP)type artificial neural network(ANN). The trained network is then used to predict the effect of various parameters on the desired output namely the flexural tensile strength, tensile strength behavior and compressive strength.

“…In such complex problems, which are difficult to be modeled using conventional modeling techniques, the application of ANNs may have a good application potential. ANNs have been introduced to the field of civil engineering as a powerful modeling technique, which has achieved acceptable success in many applications (Hegazy et al 1998;Perera et al 2010;Bashir and Ashour 2012;Mashrei et al 2013;Lee and Lee 2014). ANNs are numerical architectures composed of huge elements of strongly interlocking manufactured elements known as neurons, which simulate the human brain mechanism of learning and solving problems.…”

Section: Latin American Journal Of Solids and Structures 13 (2016) 14mentioning

confidence: 99%

Influence of Fiber Properties on Shear Failure of Steel Fiber Reinforced Beams Without Web Reinforcement: ANN Modeling

Abbas

Khan

2016

In this paper, an artificial neural network (ANN-10) model was developed to predict the ultimate shear strength of steel fiber reinforced concrete (SFRC) beams without web reinforcement. ANN-10 is a four-layered feed forward network with a back propagation training algorithm. The experimental data of 70 SFRC beams reported in the technical literature were utilized to train and test the validity of ANN-10. The input layer receives 10 input signals for the fiber properties (type, aspect ratio, length and volume content), section properties (width, overall depth and effective depth) and beam properties (longitudinal reinforcement ratio, compressive strength of concrete and shear span to effective depth ratio). ANN-10 has exhibited excellent predictive performance for both training and testing data sets, with an average of 1.002 for the average of predicted to experimental values. This performance of ANN-10 established the promising potential of Artificial Neural Networks (ANNs) to simulate the complex shear behavior of SFRC beams. ANN-10 was applied to investigate the influence of the fiber volume content, type, aspect ratio and length on the ultimate shear strength of SFRC.