An Ensemble Learning Prediction Method for Shear Strength of Steel Fiber Reinforced Concrete Beams

DI Chunfeng

doi:10.13204/j.gyjzG21112303

Volume 53 Issue 11

Nov. 2023

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2023 > 53(11): 139-144

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DI Chunfeng. An Ensemble Learning Prediction Method for Shear Strength of Steel Fiber Reinforced Concrete Beams[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(11): 139-144. doi: 10.13204/j.gyjzG21112303

Citation:

DI Chunfeng. An Ensemble Learning Prediction Method for Shear Strength of Steel Fiber Reinforced Concrete Beams[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(11): 139-144. doi: 10.13204/j.gyjzG21112303

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An Ensemble Learning Prediction Method for Shear Strength of Steel Fiber Reinforced Concrete Beams

doi: 10.13204/j.gyjzG21112303

DI Chunfeng

China Railway Construction Group Co., Ltd., Beijing 100043, China

Received Date: 2021-11-23

Abstract

Abstract

Traditional empirical methods to predict the shear strength of steel fiber reinforced concrete (SFRC) beams are based on limited data and difficult to deal with strong nonlinear problems, so it is difficult to predict the shear strength effectively and quickly. A shear strength prediction method of SFRC beams based on ensemble learning was developed. The data included 330 groups of beams from literature. The input parameters included the effective size of the beam, longitudinal reinforcement ratio, concrete compressive strength, aggregate size, shear-span ratio, steel fiber factor and tensile strength of fibers. Firstly, the thermodynamic diagram and correlation coefficient of the data set were analyzed, and it was found that there was almost no redundancy between the input parameters, which could be used as effective input parameters for modeling, and the linear relations between the input and output parameters was obtained; then the data set was divided into test set and training set for different ensemble learning models to calculate and record the running process; finally, the results were compared with traditional regression methods. The results showed that the accuracy of the GradientBoost algorithm in predicting the shear strength in the ensemble learning was the highest, reaching 0.950, which was higher than the average accuracy of the traditional regression method. It proved that the method could be used to predict the shear strength of SFRC.
- steel fiber reinforced concrete,
- shear strength,
- ensemble learning,
- thermodynamic diagram

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References

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