Investigation on Interfacial Bond Strength Characteristics of Concrete Filled in Galvanized Steel Tubes Utilizing Statistical Analysis and Advanced Prediction Techniques
Document Type
Article
Publication Title
Civil Engineering and Architecture
Abstract
Present investigation examines the effects of concrete and steel tube diameter, thickness, and L/D ratio (Length to Diameter ratio) on the strength of the interfacial connection in Concrete-Filled Galvanized Steel Tubes (CFGST). By using three levels and variables to conduct the experiments, Taguchi's technique is used in the study to shorten the experimentation procedure. In the beginning, the Taguchi technique was used to frame the L9 array, and nine circular CFGSTs were tested to gauge the bonding strength. A total of 81 samples were evaluated to determine the correctness of a linear regression model, which had been built. The experimental data were further analyzed using analysis of variance in order to determine the factors that may have an impact on bonding strength. When the experimental findings were compared to a model of finite element analysis and a soft computing tool of artificial neural networks, the inaccuracies were found to be a maximum of 18% thereby confirming accuracy up to 82%. Overall, the results present that the L/D ratio followed by thickness and diameter of the steel tubes are significant factors which have the greatest negative impact on decremental bond strength characteristics. Further prediction is carried out by, Ansys Mechanical finite element analysis software (ANSYS), Artificial Neural Network (ANN), which showed that ANN produces more significant outcomes as compared to ANSYS, and hence is suggested for real time practice.
First Page
1786
Last Page
1799
DOI
10.13189/cea.2024.120338
Publication Date
5-1-2024
Recommended Citation
Chethan Kumar, S.; Kumar, N. S.; Tantri, Adithya; and Bhandary, R. P., "Investigation on Interfacial Bond Strength Characteristics of Concrete Filled in Galvanized Steel Tubes Utilizing Statistical Analysis and Advanced Prediction Techniques" (2024). Open Access archive. 6598.
https://impressions.manipal.edu/open-access-archive/6598