Given the importance of using steel fibers in reinforcement of concrete, in the present study using artificial neural networks to predict the behavior of hook -shaped steel fibers from concrete. Due to the constraint of comprehensive laboratory data, data obtained from More
Given the importance of using steel fibers in reinforcement of concrete, in the present study using artificial neural networks to predict the behavior of hook -shaped steel fibers from concrete. Due to the constraint of comprehensive laboratory data, data obtained from limited element analysis has been used as neural network input. The fibers are used to simulate the fiber and the Abacus software. In the limited element model, the interactions between fibers and concrete are simulated using the concept of the transitional area of the common surface whose parameters are obtained using the reversed limited element method and the use of the out -of -the -way experimental test results on a fiber sample. After assessment of the numerical model results with the empirical results, the results were extracted for effective parameters of the fibers and based on them using neural networks. Forecasting of the outburst has been carried out by the Multi-Layer Artificial Neural Networks and the Rear Publishing Algorithm, with Marcoradet-Clberg optimization techniques. The results show that the neural network model presented in this study, due to the ability to use more variables in modeling and more accurate results, is an effective way to predict the fiber's extrusion force.
Manuscript profile
The main purpose of this study is to develop a finite element model to study the effect of steel-shaped fibers on the flexural strength of ultra-high performance fiber concrete. For this purpose, in order to numerically simulate, a multi-scale finite element model was d More
The main purpose of this study is to develop a finite element model to study the effect of steel-shaped fibers on the flexural strength of ultra-high performance fiber concrete. For this purpose, in order to numerically simulate, a multi-scale finite element model was developed in which concrete was modeled as a homogeneous and uniform material and steel fibers were randomly distributed inside it. In order to make more realistic assumptions, the area of adhesion between the fibers and the concrete is also considered. After validating the results of finite element model with the results of experimental tests, the effect of parameters such as volume fraction and adhesion of steel fibers on the strength characteristics of flexural strength of ultra-high performance concrete reinforced with fibers has been studied numerically. The results indicate that the effect of using fibers on the amount of energy absorption by fiber reinforced concrete is much greater than its effect on other characteristics of this type of concrete and especially the use of fibers in low fiber volumes has a very significant effect on energy absorption so that for 0.5% of the volume fraction of steel fibers, an increase of about 17 times compared to the sample of super-reinforced concrete without fibers can be resulted
Manuscript profile
In the modern characteristics of concrete design based on daily needs, the use of recycled materials is an important and basic principle. Therefore, in the present study, PET (Polyethylene Terephthalate) has been substituted for fine aggregates in self-compacting concre More
In the modern characteristics of concrete design based on daily needs, the use of recycled materials is an important and basic principle. Therefore, in the present study, PET (Polyethylene Terephthalate) has been substituted for fine aggregates in self-compacting concrete. The aim of this study is to produce and optimize the mechanical and rheological properties of environmentally friendly self-compacting concretes. Input variables in the mixtures include (PET) as a substitute for a percentage of fine aggregates, steel fibers, powder stone as a substitute for a percentage of cement weight, and lubricant as a percentage of powder material weight. The studied responses are slump flow, L-box ratio (H2 / H1), compressive and tensile strengths. Mixing schemes were designed and studied using the Central Composite Design (CCD) method, which is one of the RSM (Response Surface Methodology) methods. The results demonstrated that with increasing PET, the rheological and mechanical properties of the mixtures decreased while the fibers effectively improved the reduction of strengths. Applying mathematical models provided by ANOVA, multi-objective optimizations were performed to maximize compressive strength by the RSM method and an optimal mixing scheme based on experimental results was proposed.
Manuscript profile
As steel fibers are important reinforcement materials in concrete, in this study, the behavior of hook-shaped steel fibers from concrete is predicted through the use of artificial neural networks. In the absence of comprehensive laboratory data, data obtained from finit More
As steel fibers are important reinforcement materials in concrete, in this study, the behavior of hook-shaped steel fibers from concrete is predicted through the use of artificial neural networks. In the absence of comprehensive laboratory data, data obtained from finite element analysis was used for modeling. The simulations are carried out using ABAQUS software's finite element method in 3D. Using the concept of the transition zone of the interface, whose parameters were obtained by inverse finite element analysis and experimental tests conducted on a sample of fibers, this model has been developed to simulate the interaction between fibers and concrete. On the basis of the results of the numerical model validated against the experimental results, the effective parameters of the fibers were extracted, and a neural network was then constructed based on the results. A multilayer forward perceptron artificial neural network and back-propagation training algorithm are used to predict pull-out force, with Marquardt-Lonberg optimization applied. The results demonstrate that the neural network model presented in this research is an effective method for predicting the pull-out force of fibers from concrete, in part because it allows the use of more variables in modeling, as well as delivering more accurate results.
Manuscript profile
Sanad
Sanad is a platform for managing Azad University publications
