Shear Strength Prediction of FRP-reinforced Concrete Beams Using an Extreme Gradient Boosting Framework

Abstract

Despite the importance and accuracy of empirical models, most of the existing models are only accurate on the collected experimental data. Adding new data, or even considering noise or variance in the data leads to loss of model accuracy. The objective of this paper is to alleviate overfitting and develop a more accurate and reliable alternative method using a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework for the prediction of the ultimate shear strength of FRP-reinforced concrete beams without stirrups. To enhance the robustness of the results, make full use of training samples (without the validation set), and alleviate the randomness in selecting test samples, the K-Fold Cross Validation method is employed. Using a dataset including 205 samples, results show that the extreme gradient boosting framework (XGBoost) providing better prediction. In fact, XGBoost results have higher precision and higher generalization in comparison with the empirical equations, the current design codes of practice, Least Absolute Shrinkage and Selection Operator model (LASSO), and Random Forest model (RF).

Prediction model for compressive strength of rice husk ash blended sandcrete blocks using a machine learning models

“…However, the activation function (f) plays a crucial role in transforming the weighted sum of inputs (z) in each neuron: a = f(z). (Kaveh et al, 2022). Each tree learns from the errors of the previous one, resulting in improved prediction accuracy.…”

Shear strength prediction of reinforced concrete beams using machine learning

“…The result indicates that the proposed model was able to make superior predictions compared to the remaining methods. Ali et al [58] used the extreme gradient boosting method (XGBoost) for developing a shear prediction model for FRP-reinforced concrete beams without transverse reinforcements. To utilise the complete dataset K-fold cross-validation technique was implemented in this investigation.…”

“…It was also observed that the UHPC and FRC models available in the literature underestimated shear capacity. Kaveh et al [84] proposed novel ML models for predicting the shear strength of beams without stirrups and reinforced with FRP bars using three different algorithms. They employed LASSO regression, RF, and XGBoost to develop new prediction models.…”

“…Therefore, the user should be well aware of their strengths and weaknesses. Although different comparative studies are available on the use of various algorithms to solve different structural engineering problems, the conclusions arrived at by the researchers are observed to be contradictory [64,74,80,84,89,[106][107][108]. Extensive research is required to understand and highlight the feasibility and efficiency of various algorithms in solving different structural engineering problems.…”

Investigating the Bond Strength of FRP Rebars in Concrete under High Temperature Using Gene-Expression Programming Model

“…Nonetheless, the traditional AI methods such as ANN have a few shortcomings such as local minima trap issue [ 32 , 33 , 34 , 35 ]. In order to circumvent these issues, researchers utilised various other AI methods such as support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), ensemble trees (ET), and extreme gradient boosting (EGB), etc., in predicting the strength of FRP-reinforced concrete [ 36 , 37 , 38 , 39 ]. Chen et al [ 40 ] developed an ensemble learning-based gradient-boosted regression tree (GBRT) model to estimate FRP-concrete interfacial bond strength using 520 samples.…”