Research on Prediction Models of Flexural Capacity of Corroded RC Beams Based on Ensemble Learning

ZHANG Xue; MEN Jinjie; RONG Qiang; QIAO Dehao

doi:10.3724/j.gyjzG24012901

Volume 54 Issue 12

Dec. 2024

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ZHANG Xue, MEN Jinjie, RONG Qiang, QIAO Dehao. Research on Prediction Models of Flexural Capacity of Corroded RC Beams Based on Ensemble Learning[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(12): 128-137. doi: 10.3724/j.gyjzG24012901

Citation:

ZHANG Xue, MEN Jinjie, RONG Qiang, QIAO Dehao. Research on Prediction Models of Flexural Capacity of Corroded RC Beams Based on Ensemble Learning[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(12): 128-137. doi: 10.3724/j.gyjzG24012901

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Research on Prediction Models of Flexural Capacity of Corroded RC Beams Based on Ensemble Learning

doi: 10.3724/j.gyjzG24012901

1. School of Civil Engineering, Xi'an University of Architecture & Technology, Xi'an 710055, China;
2. Key Lab of Structural Engineering and Earthquake Resistance, Ministry of Education (XAUAT), Xi'an 710055, China;
3. School of Civil Engineering, Yantai University, Yantai 264005, China

Received Date: 2024-01-29
Available Online: 2025-01-04
Publish Date: 2024-12-20

Abstract

Abstract

To quickly and accurately determine the flexural capacity of corroded reinforced concrete (RC) beams, an ensemble learning-based data-driven bearing capacity prediction model for corroded RC beams was studied. A database of experimental tests on the flexural bearing capacity of corroded RC beams was established based on existing literature. Based on the dataset, five types of ensemble learning algorithms, namely Random Forest (Random Forest), Adaptive Boosting (Adaboost), Gradient Boosting Decision Tree (GBDT), Limit Gradient Boosting Algorithm (XGBoost) and Light Gradient Boosting Algorithm (LightGBM), were used to establish prediction models. Grid search was employed to optimize the hyperparameters of the models to improve their generalization performance. The performance of different ensemble learning algorithms was compared, and the feature importance of input parameters was analyzed through the dataset. The mean absolute error (MAE), determination coefficient (R²) and root mean square error (RMSE) of the prediction models were compared to assess their rationality and accuracy. The analysis results indicated that the prediction model could effectively determine the key influencing factors of the flexural bearing capacity of corroded RC beams, namely the reinforcement ratio and the corrosion rate of the rebar. The model based on RandomForest performed the best, followed by the model based on XGBoost. The fitting degree of the prediction models on the training and test sets could reach over 90%.
- RC structures,
- prediction of bearing capacity,
- ensemble learning,
- corrosion,
- reinforced concrete beam

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References(41)

References

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