Steel bars identification in reinforced concrete structures by using ANN and magnetic fields

This work proposes a methodology for non destructive testing (NDT) of reinforced concrete structures, using superficial magnetic fields and artificial neural networks, in order to identify the size and position of steel bars, embedded into the concrete. For the purposes of this paper, magnetic induction curves were obtained by using a finite element program. Perceptron Multilayered (PML) ANNs, with Levemberg-Marquardt training algorithm were used. The results presented very good agreement with the expect ones, encouraging the development of real systems based upon the proposed methodology.

A finite element approach for predicting the ultimate rotation capacity of RC beams

This paper presents a numerical approach to model the complex failure mechanisms that define the ultimate rotational capacity of reinforced concrete beams. The behavior in tension and compression is described by a constitutive damage model derived from a combination of two specific damage models [1]. The nonlinear behavior of the compressed region is treated by the compressive damage model based on the Drucker-Prager criterion written in terms of the effective stresses. The tensile damage model employs a failure criterion based on the strain energy associated with the positive part the effective stress tensor. This model is used to describe the behavior of very thin bands of strain localization, which are embedded in finite elements to represent multiple cracks that occur in the tensioned region [2]. The softening law establishes dissipation energy compatible with the fracture energy of the concrete. The reinforcing steel bars are modeled by truss elements with elastic-perfect plastic behavior. It is shown that the resulting approach is able to predict the different stages of the collapse mechanism of beams with distinct sizes and reinforcement ratios. The tensile damage model and the finite element embedded crack approach are able to describe the stiffness reduction due to concrete cracking in the tensile zone. The truss elements are able to reproduce the effects of steel yielding and, finally, the compressive damage model is able to describe the non-linear behavior of the compressive zone until the complete collapse of the beam due to crushing of concrete. The proposed approach is able to predict well the plastic rotation capacity of tested beams [3], including size-scale effects.

Comparative analysis of strut-and-tie models using Smooth Evolutionary Structural Optimization

The strut-and-tie models are widely used in certain types of structural elements in reinforced concrete and in regions with complexity of the stress state, called regions D, where the distribution of deformations in the cross section is not linear. This paper introduces a numerical technique to determine the strut-and-tie models using a variant of the classical Evolutionary Structural Optimization, which is called Smooth Evolutionary Structural Optimization. The basic idea of this technique is to identify the numerical flow of stresses generated in the structure, setting out in more technical and rational members of strut-and-tie, and to quantify their value for future structural design. This paper presents an index performance based on the evolutionary topology optimization method for automatically generating optimal strut-and-tie models in reinforced concrete structures with stress constraints. In the proposed approach, the element with the lowest Von Mises stress is calculated for element removal, while a performance index is used to monitor the evolutionary optimization process. Thus, a comparative analysis of the strut-and-tie models for beams is proposed with the presentation of examples from the literature that demonstrates the efficiency of this formulation. © 2013 Elsevier Ltd.