%0 Journal Article
%T Prediction of shear strength of deep beams of the reinforced concrete using weighted least squares support vector machine method
%J Amirkabir Journal of Civil Engineering
%I Amirkabir University of Technology
%Z 2588-297X
%A Mohammadizadeh, Mohammad Reza
%A Esfandnia, Farnaz
%D 2021
%\ 11/22/2021
%V 53
%N 9
%P 3867-3882
%! Prediction of shear strength of deep beams of the reinforced concrete using weighted least squares support vector machine method
%K Deep beam of reinforced concrete
%K Shear Strength
%K Artificial Intelligence
%K Weighted least squares support vector machine
%R 10.22060/ceej.2020.18122.6775
%X The shear strength of deep reinforced concrete beams depends on the mechanical and geometrical properties of the beam. Accurate estimation of shear strength in deep reinforced concrete beams is one of the major issues in the design of engineering structures. However, some methods proposed to determine the shear strength in deep reinforced concrete beams do not have high accuracy. One method to accurately estimate shear strength is to use artificial intelligence (AI). Artificial intelligence has many different methods, one of which is the use of artificial intelligence-based on the support vector machine method. In this study, the weighted least squares support vector machine (WLS-SVM), which is a relatively new and efficient method for predicting the shear capacity of reinforced concrete beams, has been used. In this study, a database containing experimental results on deep reinforced concrete beams was first collected. Then, after determining the input and output parameters using a training process in WLS-SVM method and using a part of the collected data, a model was developed to predict the shear strength of deep reinforced concrete beams. In order to determine the accuracy of the WLS-SVM method, the results were compared with those obtained by other AI methods and different regulations. Statistical analysis showed that WLS-SVM has the best performance in terms of statistical evaluation parameters (R2 = 0.9887, RMSE = 0.107, MAE = 0.478 and MAPE = 9.48%) compared to the other method.
%U https://ceej.aut.ac.ir/article_4167_1cd389d1310b5a417b54c924943a307e.pdf