Modeling of confined circular RC columns using artificial neural network and finite element method

Obaidat, Yasmeen Taleb; Barham, Wasim S.; Obaidat, Ala’ Taleb; Al-Khazaaleh, Nour Aleslam A.

doi:10.1016/j.istruc.2022.04.018

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…FRP is mainly used for reinforcing RC columns and lateral confinement since it significantly enhances the performance of concrete structures when subjected to axial stresses. Confinement, especially when wrapping a column with circumferentially oriented FRP, significantly enhances the load capacity and ductility of RC columns [6]. Moreover, the outer confinement provided by the FRP wrapping increases the members' durability and prevents corrosion of the core concrete.…”

Section: Introductionmentioning

confidence: 99%

“…They found that the load-bearing capacity (LBC) and deformation capacities of the laden columns were enhanced by FRP reinforcement. Furthermore, the LBC of the columns improved with FRP layers, mainly when columns were enclosed by a circular FRP wrap [6]. Fardis investigated the influence of concrete core's compressive strength (CS) and cross-sectional dimensions on the axial compressive behavior of FRP-confined circular concrete columns.…”

Section: Introductionmentioning

confidence: 99%

“…However, using ML to determine the MLC of FRP-confined CRCC is still in the research and development phase, with very few scientific studies being conducted. In a specific study by Obaidat et al [6], an Artificial Neural Network (ANN) model was utilized to predict the ultimate load based on a dataset comprising 92 FRP-confined CRCC. The performance of the ANN model is assessed using R 2 .…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Maximum Load Capacity of Circular RC Columns Confined with Fibre-Reinforced Polymer (FRP) Using Machine Learning Model

Indra Prakash,

Thuy Anh Nguyen

2023

JSTT

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This article conducts an exhaustive investigation into the utilization of machine learning (ML) methods for forecasting the maximum load capacity (MLC) of circular reinforced concrete columns (CRCC) using Fiber-Reinforced Polymer (FRP). Extreme Gradient Boosting (XGB) algorithm is combined with novel metaheuristic algorithms, namely Sailfish Optimizer and Aquila Optimizer, to fine-tune its hyperparameters. The robustness and generalizability of these optimized hyperparameters are ensured through 200 Monte Carlo simulations (MCS). The model is constructed based on a database of 207 experimental results. Its performance is evaluated using three criteria: root mean squared error, mean absolute error, and the coefficient of determination. This study includes a performance comparison of the XGB4 model with eight other ML models, namely CatBoost (CAT), Gradient Boosting (GB), Hist Gradient Boosting (HGB), default XGB, Light Gradient Boosting (LGB), Linear Regression (LR), and Random Forest (RF). This comparison identifies the most effective model for predicting the MLC of columns. Additionally, this study explores the interpretability of the XGB model by SHAP values. This analysis illuminates the significance and interactions of various input features in predicting the FRP-confined CRCC's MLC. It offers insights into the primary elements influencing structural behavior by displaying a graphical depiction of the impact of specific characteristics on the model's output. This study culminates in developing an interactive Graphical User Interface (GUI) based on the XGB model. This tool allows users to investigate the influence of input parameters on the predicted MLC values, thereby enhancing their understanding and application of the model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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