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ANN-Based Modeling of Shear Behavior of Reinforced Concrete Columns under Constant Axial Loads | ||
| Civil Engineering and Applied Solutions | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 29 آبان 1404 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22080/ceas.2025.30228.1050 | ||
| نویسندگان | ||
| Maedeh Sadeghpour Haji1؛ Javad Shayanfar* 2؛ Reza niknam1 | ||
| 1Department of Civil Engineering, Islamic Azad University, Qaemshahr, Iran | ||
| 2Department of Civil Engineering, University of Minho, Azurém, Guimarães, Portugal | ||
| تاریخ دریافت: 15 مهر 1404، تاریخ بازنگری: 22 آبان 1404، تاریخ پذیرش: 27 آبان 1404 | ||
| چکیده | ||
| This research introduces an artificial neural network (ANN)–based analytical framework for accurately modeling the nonlinear shear behavior of reinforced concrete (RC) columns subjected to constant axial loads. Many existing RC buildings, designed under outdated seismic provisions, exhibit inadequate shear capacity that often results in brittle column failures during earthquakes. To overcome this limitation, a combined analytical and computational model was developed by integrating a fiber-based flexural formulation with a Mohr’s circle–based shear model incorporating a ductility-dependent degradation parameter. A comprehensive experimental database of 164 RC column tests, covering a wide range of geometric, material, and loading parameters, was used to train and validate the ANN. The model predicts principal tensile stresses and shear strengths with excellent accuracy, outperforming conventional analytical approaches. Statistical evaluation confirmed the ANN’s robustness and strong correlation with experimental data. The proposed ANN-based model offers an efficient and reliable tool for seismic performance assessment, retrofitting design, and code development of deficient RC columns | ||
| کلیدواژه‌ها | ||
| Reinforced concrete columns؛ Shear behavior؛ Artificial neural networks (ANN)؛ Variable axial load؛ Nonlinear modeling؛ Seismic performance | ||
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