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A platform for research: civil engineering, architecture and urbanism
Evaluation and modeling of ultimate bond strength of corroded reinforcement in reinforced concrete elements
Abstract Bond deterioration between the reinforcement and surrounding concrete is one of the crucial reasons for the structural degradation in the steel–concrete composite structures. The assessment of bond deterioration due to corrosion is of prime importance on this issue. This study aims to present the derivation of analytical formulation of ultimate bond strength τ u at the corroded reinforcement–concrete interface for the reinforced concrete (RC) elements subjected to various levels of corrosion. The modeling technique dealt in this work is gene-expression programming and artificial neural network. The data used for the development of the models are thoroughly selected from the available experimental studies reported in the technical literature. A total of 218 experimental data samples were arranged to obtain training and testing data sets. The critical predictive factors were compressive strength of concrete, concrete cover, steel type, diameter of the steel bar, bond length, and corrosion level. The performances of the proposed empirical models were also evaluated statistically. The results indicated that the soft-computing based models had a satisfactory performance to predict the ultimate bond strength of corroded steel bars in RC elements.
Evaluation and modeling of ultimate bond strength of corroded reinforcement in reinforced concrete elements
Abstract Bond deterioration between the reinforcement and surrounding concrete is one of the crucial reasons for the structural degradation in the steel–concrete composite structures. The assessment of bond deterioration due to corrosion is of prime importance on this issue. This study aims to present the derivation of analytical formulation of ultimate bond strength τ u at the corroded reinforcement–concrete interface for the reinforced concrete (RC) elements subjected to various levels of corrosion. The modeling technique dealt in this work is gene-expression programming and artificial neural network. The data used for the development of the models are thoroughly selected from the available experimental studies reported in the technical literature. A total of 218 experimental data samples were arranged to obtain training and testing data sets. The critical predictive factors were compressive strength of concrete, concrete cover, steel type, diameter of the steel bar, bond length, and corrosion level. The performances of the proposed empirical models were also evaluated statistically. The results indicated that the soft-computing based models had a satisfactory performance to predict the ultimate bond strength of corroded steel bars in RC elements.
Evaluation and modeling of ultimate bond strength of corroded reinforcement in reinforced concrete elements
Güneyisi, Esra Mete (author) / Mermerdaş, Kasım (author) / Gültekin, Ayşegül (author)
Materials and Structures ; 49 ; 3195-3215
2015-10-10
21 pages
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2016
Bond strength modeling for corroded reinforcement in reinforced concrete
| British Library Online Contents | 2004