Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study

Abstract

Fiber reinforced polymer (FPR) bars have been widely used as a substitutional material of steel reinforcement in reinforced concrete elements in corrosion areas. Shear resistance of FRP reinforced concrete element can be affected by concrete properties and transverse FRP stirrups. Hence, studying the shear strength (Vs) mechanism is one of the highly essential for pre-design procedure for reinforced concrete elements. This research examines the ability of three machine learning (ML) models called M5-Tree (M5), extreme learning machine (ELM), and random forest (RF) in predicting Vs of 112 shear tests of FRP reinforced concrete beam with transverse reinforcement. For generating the prediction matrix of the developed ML models, statistical correlation analysis was conducted to generate the suitable inputs models for Vs prediction. Statistical evaluation and graphical approaches were used to evaluate the efficiency of the proposed models. The results revealed that all the proposed models performed in general well for all the input combinations. However, ELM-M1 and M5-Tree-M5 models exhibited less accuracy performance in comparison with the other developed models. The study showed that the best prediction performance was revealed by M5 tree model using nine input parameters, with coefficient of determination (R2) and root mean square error (RMSE) equal to 0.9313 and 35.5083 KN, respectively. The comparison results also indicated that ELM and RF were performed significant results with a less slight performance than M5 model. The study outcome contributes to basic knowledge of investigating the impact of stirrups on Vs of FRP reinforced concrete beam with the potential of applying different computer aid models.

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

“…To obtain an optimised ANN model, the value of "R" should be highest (approaching 1), while the values of "MSE" and "MAE" should be lowest [30]. To overcome the problem of overfitting, the gradient descent methodology is used to converge values of weights and biases, while, at the same time, early stopping criteria, as defined in the functionality of ANN, is employed to avoid overfitting [31][32][33].…”

The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

“…The shear capacity of RC beams was predicted mathematically using a variety of ML approaches [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. The use of the well-known artificial neural network (ANN) technique was adopted to investigate the impact of various crucial variables on the shear strength of FRP-RC beams [25].…”

“…It was shown that the ELM can be utilized to accurately measure the shear strength of compositestrengthened RC beams. In a more recent study [32], the predictive efficacy of three ML methods, namely, ELM, M5-Tree, and RF, was compared using the results on 112 FRPstrengthened RC beams. The results revealed that the M5-Tree model yielded slightly higher accuracy in predicting the shear strength as compared with the other two models.…”