MODELING OF FIBRE REINFORCED CONCRETE

Journal of International Society for Science and Engineering, 2019

Due to the shortage of clear equations in the building codes that explain shear strength for fiber reinforced concrete (FRC) beams; there is a need to develop a numerical approach that can be used to predict shear behavior in FRC. The main objective of this research is to develop an artificial Neural Network (ANN) that can predict shear strength and simplify its use through developing a Graphic User Interface (GUI). Moreover, shear behavior in fiber reinforced concrete beams (FRCBs) is quantified by compressive strength of concrete, longitudinal steel, size effect, fiber's type, content and aspect ratio. The research methodology is based on collecting experimental results of technical investigations carried out to predict shear behavior in FRCBs. ANN aims at reducing the amount of computing time required in the numerous iterations involving structural analysis and experimental work. For this, two back-propagation neural networks have been experimented by MATLAB program; their types have been fitting (1st network) and pattern recognition (2nd network) which are used to classify failure of FRC beams into 6 categories. Through simulation study, the optimum architectures for the individual ANNs have been determined. The training algorithms used feed forward back propagation. The ANNs model has been assessed in comparison with exact values and deduces a good correlation with it. Finally, a software program is developed as an evaluation system for predicting resistance of FRC beams to shear forces, and to expect the failure pattern in order to avoid its occurrence.

Journal of Zhejiang University SCIENCE A, 2011

The main objective of this study is to drive a simple solution for prediction of steel fiber reinforced concrete (SFRC) behavior under four point bending test (FPBT). In this study the concrete constitutive model has been supposed as a bilinear elastic-perfectly plastic stress-strain response in compression and a linear response up to tensile strength for tension. An exponential relation has been assumed for stress-crack opening in crack region. The assumed relation needs two parameters. The moment capacity is calculated by applying these assumptions and satisfying equilibrium law at critical cracked section. After that, parametric studies have been done on the behavior of SFRC. Finally the proposed model has been validated with some existing experimental test.

Computational Modelling of Concrete Structures, 2014

This work presents a novel numerical model based on the use of coupling finite elements to simulate the behavior of steel fiber reinforced concrete (SFRC) with a discrete and explicit representation of steel fibers. The material is described as a composite made up by three phases: concrete, discrete discontinuous fibers and fiber-matrix interface. The steel fibers are modeled using two-node finite elements (truss elements) with a one-dimensional elastoplastic constitutive model. They are positioned using an isotropic uniform random distribution, considering the wall effect of the mold. A non-rigid coupling procedure is proposed for modeling the complex nonlinear behavior of the fiber-matrix interface by adopting an appropriate constitutive damage model to describe the relation between the shear stress (adherence stress) and the relative sliding between the matrix and each fiber individually. An isotropic damage model including two independent scalar damage variables for describing the concrete behavior under tension and compression is considered. To increase the computability and robustness of the continuum damage models used to simulate matrix and interface behavior, an implicit-explicit integration scheme is used. Numerical examples involving a single fiber and a cloud of fibers are performed. Comparisons with experimental results demonstrate that the application of the numerical strategy for modeling the behavior of SFRC is highly promising and may constitute an important tool for better understanding the effects of the different aspects involved in the failure process of this material.

2009

Fibre Reinforced Concrete (FRC) can be very effective in precast pre-stressed high strength concrete structures, since shear reinforcement and passive longitudinal bars can be totally replaced by fibre reinforcement. To simulate adequately the fibre reinforcement benefits, material constitutive models, able of capturing the crack initiation and crack propagation need to be used, under the framework of FEM-based analysis. In the present work, the use of FRC was explored for the development of innovative structural systems for industrial buildings. The connections between structural precast elements were also simulated. The numerical simulations are described and the results are analyzed and discussed.

Fiber reinforced concrete (FRC) is a type of concrete that contains discontinuous fibers distributes randomly among the concrete block. In this paper, the Artificial Neural Networks are utilized to predict the effect of the addition of steel nails as fibers on the compressive strength of concrete. The study involves testing of cubic concrete samples with various mixing proportions and water cement ratios. The results showed that (for mixing proportion 1:1.5:3) the compressive strength has the more increasing when the fibers are added with 12%, while it has the more increasing at 20% fibers adding for mixing proportion (1:2:4). It is also found that the optimum water cement ratio is found to be 46% for the mixing proportion (1:1.5:3) with 12% fibers and 55% for mixing of (1:2:4) with fibers adding 20%. The results showed also that the increasing of the percentage of fibers added with mixing ratio (1:1.5:3) leads the compressive strength to increase more uniformly and effectively than the use of the mixing ratio (1:2:4). Also it is found that using a larger size of nails with low percent of addition will significantly increase the compressive strength with the increasing of percentage of addition the compression strength decreases.

Expert Systems with Applications, 2010

Within the framework of studies on FRC, series of tests were undertaken in the laboratory in order to better understand the behavior of FRC and composite fibers to characteristic loading. The results obtained in the tests vary according to the percentage of the fibers, the water content, the size of grains (grains size distribution) and percentage of composite fibers. Therefore, it is important to estimate the deformation of concrete corresponding to the applied load according to available data and in the case of lacking of enough experimental data. For this purpose, neural network technique was used to predict the load-displacement curve and also compressive strength of concrete based on mix proportions. At first, the results of experimental tests carried out in PWUT laboratory on fiber reinforced concrete specimens are presented and then the missing experimental data and gaps in load-displacement curve trend are predicted by back-propagation method in neural network. It is worth mentioning that it can also be used to study the different types of fibers and also orientation of the fibers which will be presented in future works.

Steel fibre reinforced concrete (FRC) has higher ductility, it can save amount of convention reinforcement, labour and in consequence costs of the structure. However, broader use of SFRC as construction material is limited among others by lack of design codes. According to the previous study, reliability and safety of ordinary reinforced engineering can be verified using nonlinear finite element analysis and several safety formats that are proposed in fib Model Code 2010. In the presented paper, safety formats are applied for fibre reinforced structures such as tunnel lining precast segment and individual approaches are compared. As tensile and shear cracks or compressive crushing can develop in the fibre reinforced concrete under severe conditions, the design combining numerical and experimental investigations together with safety formats is appropriate method how to obtain safe and reliable structure. Finite element method and advanced material models taking into account FRC properties such as shape of tensile softening branch, high toughness and ductility are described in the paper. Since the variability of FRC material properties is rather high, full probabilistic analysis seems to be the most appropriate format for evaluation of structural performance, reliability and safety.

SN Applied Sciences

The necessity of providing low-cost housing to economically weaker sections of society has been recognised by the national government of India. In mountainous areas, the use of locally available construction material, such as bamboo, as concrete reinforcement has increased due its easy availability and economic benefit. However, due to the inadequate codal provisions for the design and detailing of bamboo-reinforced structures, evaluating the serviceability criteria for their deflection behaviour under different loads is difficult. Furthermore, factors such as bond failure between reinforcement and concrete, shrinkage and corrosion of reinforcing material, and uncertainty in material strength make the prediction of deflection even more cumbersome. This study presents an artificial neural network (ANN)-based method modelled using MATLAB for predicting the deflection behaviour of three types of beams, namely plain, steel-reinforced, and bamboo-reinforced beams. Experimental investigation is conducted to record data at regular load increments for the aforementioned three beam typologies fabricated in the laboratory under two-point loading for 28 days. A total of 122 laboratory test data are recorded for modelling the ANN. The used approach involves predicting the relationship among the applied load, tensile strength of the reinforcement, percentage (amount) of reinforcement (taken as input), and deflection of the beam (obtained as output). The present ANN approach exhibits gives satisfactory performance (coefficient of determination (R 2) = 0.9983 and mean square error = 0.00049) in predicting the deflection behaviour of beams. Hence, the ANN approach can be used as an efficient and robust tool in predicting serviceability behavior of different types of reinforced concrete beams.

2019

This report summarizes the first results of 2D and 3D finite element method numerical modelling analysis of fiber-reinforced concrete behaviour and a tunnel case study application. A porosity strength-dependant material model was assumed and preliminary calibrated to fit the mechanical performance of fiber-reinforced concrete stress and crack opening displacement measured results.

Civil Engineering and Architecture, 2022

This study presents an approach for the prediction of the shear strength of steel fiber reinforced concrete (SFRC) beams using the Artificial Neural Network (ANN) developed based on existing experimental shear resistance results from various researchers. The experimental results database containing 42 sample numbers of SFRC beams (with shear span-to-depth ratio exceeding 2.5) without stirrups, with compressive strength of concrete varying from 24.9 to 68.6 MPa and steel fibers of hooked end type are used to develop an ANN model. The developed ANN model is trained by using 70% and 90% of the data and another 30 to 10% served as the validation data purpose. The shear strengths prediction based on ANN model was found to be in perfect agreement with the experimental values when the optimal neuron number is 2 and by fixing the training set size as 90%. Results showed that this ANN model has strong potential as a feasible design tool for predicting the shear strength of SFRC beams without transverse reinforcement or stirrups within the range of input parameters considered in this study.