Coupled extreme gradient boosting algorithm with artificial intelligence models for predicting compressive strength of fiber reinforced polymer- confined concrete

Accurately predicting and identifying appropriate parameters are necessary for producing a safe and reliable strength model of concrete elements confined with fiber-reinforced polymers (FRP). In this study, an extreme gradient boosting (XGBoost) algorithm was developed for the feature selection and...

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Veröffentlicht in:Engineering applications of artificial intelligence Jg. 134; S. 108674
Hauptverfasser: Tao, Hai, Ali, Zainab Hasan, Mukhtar, Faisal, Al Zand, Ahmed W., Marhoon, Haydar Abdulameer, Goliatt, Leonardo, Yaseen, Zaher Mundher
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.08.2024
Schlagworte:
Artificial intelligence
Composite system
Confined concrete
Extreme gradient boosting
Fiber reinforced polymer
Composite system
Extreme gradient boosting
Fiber reinforced polymer
Artificial intelligence
Confined concrete
ISSN:0952-1976, 1873-6769
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Zusammenfassung:Accurately predicting and identifying appropriate parameters are necessary for producing a safe and reliable strength model of concrete elements confined with fiber-reinforced polymers (FRP). In this study, an extreme gradient boosting (XGBoost) algorithm was developed for the feature selection and prediction of the ultimate compressive strength of FRP-confined concrete. The modeling process was established using a dataset from open-source literature consisting of 490 circular columns. Three well-known artificial intelligence (AI) models, the multivariate adaptive regression spline (MARS), extreme learning machine (ELM), and RANdom Forest GEnRator (Ranger), were used to validate the proposed model. The results demonstrated the effectiveness of the XGBoost algorithm in the modeling process, selection of suitable parameters, and enhancement of the prediction accuracy. The algorithm achieved excellent prediction results for all input combinations with a coefficient of determination (R2) greater than 0.9, and the best performance is gained by using five input parameters with (R2 = 0.955), mean absolute percentage error (MAPE = 0.130), and root mean square error (RMSE = 0.572). The study revealed the flexibility and efficiency of capturing the nonlinear behavior of complex FRP-confined concrete using the proposed model. •Compressive strength of fiber-reinforced polymer-confined concrete was predicted.•Hybrid machine learning (ML) models were developed for the learning process.•ML models developed based on a dataset consisting of 490 circular columns.•Extreme gradient boosting (XGBoost) algorithm was used as robust feature selection.•The proposed hybrid ML model was provided robust prediction model.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2024.108674