Interfacial bond capacity prediction of concrete-filled steel tubes utilizing artificial neural network

Abstract

Concrete-filled steel tubes (CFSTs) have demonstrated superior performance compared to other types of composite columns. The evaluation of interactions between concrete and steel composites is crucial due to their significant impact on the overall structural behavior in various loadings. This study develops an artificial neural network (ANN) that predicts the ultimate interfacial bond strength ($P_u$) of circular and square CFSTs. The length of the interfacial bond between the tube and the concrete; the thickness, shape, and inner perimeter of the tube; and the cubic compression strength and age of the concrete are considered as model inputs. The modeling process uses 397 experimental datasets from 18 studies of push-out tests, more specifically, 143 square and 254 circular CFSTs. ANN with a hidden layer of error-propagation, feed-forward, and sigmoidal activation function is trained, tested, optimized, and validated to achieve a good estimate. The resulting model can predict the $P_u$ with a satisfactory coefficient of determination (R²) of 0.87. The ability of the developed model to predict the $P_u$ is compared to the proposed formulas in the literature. It is found that the ANN model provides the most accurate predictions among all suggested formulas, in terms of R² and Taylor diagram analyses. Furthermore, the inclusion of the shape factor enabled the ANN model to predict the $P_u$ of both squared and circular shapes of CFSTs. The validated ANN model is then used to examine the sensitivity of the parameters to the $P_u.$ The correlations agreed well with the expected trend of published studies.

Keywords:

• Concrete-filled steel tubes
• artificial neural networks
• interfacial bond capacity
• load capacity
• shear stress
• predictive model

Reviewing Editor:

• Sanjay Kumar Shukla
• School of Engineering
• Edith Cowan University
• AUSTRALIA

Subjects:

• Stress Analysis
• Machine Learning
• Concrete & Cement

Acknowledgments

Does not apply.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

No funding received for this work.

Notes on contributors

Hatem H. Almasaeid

Dr. Hatem Almasaeid holds the position of assistant professor in civil engineering at Al al-Bayt University in Mafraq, Jordan. He earned his BS and MS degrees from Jordan University of Science and Technology in 2013 and 2015, respectively, before completing his PhD at the University of Mississippi in 2018. His research pursuits encompass a range of interests, prominently featuring applications of Artificial Neural Networks (ANN), alongside a focus on monitoring, assessing, and retrofitting concrete structures.

Donia G. Salman

Donia Salman is a Ph.D. Student in the department of civil Engineering at the University of Mississippi, Oxford, MS. She received her BS from Hashemite University, Zarqa, Jordan, in 2014 and her MS from the University of Jordan, Amman, Jordan, in 2018. Her research interests include construction and building materials, materials modeling, and finite element analysis.

Raed M. Abendeh

Raed Abendeh is Associate Professor in the department of civil and infrastructure engineering at Al-Zaytoonah University of Jordan, Amman, Jordan. He received his BS and MS from Jordan University of Science and Technology, Irbid, Jordan, in 1998 and 2001, and his Ph.D. from Technical University of Hamburg, Hamburg, Germany. His research interests include construction and building materials and finite element analysis.

Rabab A. Allouzi

Rabab Allouzi is a professor of civil engineering at the University of Jordan in Amman, Jordan. She received her BS and MS from the University of Jordan in 2008 and 2010 and PhD from Purdue University in 2015. Her research interests include seismic response of reinforced concrete structures, design of innovative structural systems, nanotechnology in structures and finite element analysis of structural systems.

Hesham S. Rabayah

Hesham Rabayah is Associate professor in the department of civil and infrastructure engineering at Al-Zaytoonah University of Jordan, Amman, Jordan. He received his MS from Jordan University of Science and Technology, Irbid, Jordan, in 1997, his MS from Amman Arab University for Graduate Studies, Amman, Jordan, in 2005, and his Ph.D. from University of Birmingham, Birmingham, West Midland, England, UK, in 2010. His research interests include sustainability, construction materials, and management. He was EPPM association president from 2020-2022.