Computational optimized finite element modelling of mechanical interaction of concrete with fiber reinforced polymer

Abstract

This paper presents a computational rational model to predict the ultimate and optimized load capacity of reinforced concrete (RC) beams strengthened by a combination of longitudinal and transverse fiber reinforced polymer (FRP) composite plates/sheets (flexure and shear strengthening system). Several experimental and analytical studies on the confinement effect and failure mechanisms of fiber reinforced polymer (FRP) wrapped columns have been conducted over recent years. Although typical axial members are large-scale square/ rectangular reinforced concrete (RC) columns in practice, the majority of such studies have concentrated on the behavior of small-scale circular concrete specimens. A high performance concrete, known as polymer concrete, made up of natural aggregates and an orthophthalic polyester binder, reinforced with non-metallic bars (glass reinforced polymer) has been studied. The material is described at micro and macro level, presenting the key physical and mechanical properties using different experimental techniques. Furthermore, a full description of non-metallic bars is presented to evaluate its structural expectancies, embedded in the polymer concrete matrix. In this paper, the mechanism of mechanical interaction of smooth and lugged FRP rods with concrete is presented. A general modeling and application of various elements are demonstrated. The contact parameters are defined and the procedures of calculation and evaluation of contact parameters are introduced. The method of calibration of the calculated parameters is presented. Finally, the numerical results are obtained for different bond parameters which show a good agreement with experimental results reported in literature

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

“…In another attempt, Behnood et al [31] have also predicted compressive strength of silica fume concrete based on a hybrid model involving ANN and multi-objective grey wolves technique. Based on these mentioned studies, it can be stated that the AI based methods could be able to analyze the nonlinear relationship between ingredients and compressive strength of various types of concrete for better prediction and assessment [32][33][34][35][36].…”