• Magazine of the Korea Concrete Institute
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• v.8 no.3
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• pp.187-195
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• 1996

A finite element formulation based on the CFT(Compression Field Theory), considering the effect of compression softening in cracked concrete, and macro-scopic and rotating crack models etc., was presented for the nonlinear behaviour of structural concrete. Considering the computational efficency and the ability of modelling the post-ultimate behaviour as major concerns, the Incremental displacement solution algorithm involving initial material stiffnesses and the relaxation procedure for fast convergence was adopted and formulated in a type of 8-noded quadrilateral isoparametric elements. The analysis program NASCOM(Non1inear Analysis of Structural Concrete by FEM : Monotonic Loading) developed in this way enables the predictions of strength and deformation capacities in a full range, crack patterns and their corresponding widths, and yield extents of reinforcement. As the verification purpose of NASCOM, the predictions were made for Bhide's Panel(PB21) and Leonhardt's deep beam tests. The predicted results shows somewhat stiff behaviour for the panel test, and vice versa for deep beam tests. More refining process would be necessary hereafter in terms of more accurately simulating the effects of tension-stiffening and compression softening in concrete.

• Computers and Concrete
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• v.22 no.2
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.