Abstract
The shear failure of reinforced concrete (RC) beams is a critical issue and has attracted the attention of researchers. The specific challenges of shear failure are the numerous factors affecting shear strength, the nonlinear behavior, and the nonlinear relationship between affecting parameters and the concrete properties. This study tackles this challenge by employing Artificial Neural Network (ANN) models. Since, according to No Free Lunch theorem, the performance of optimization algorithms is problem-dependent, this paper aims to assess the feasibility of modeling the shear strength of RC beams using ANNs trained with the Tabu Search Training (TST) algorithm. To this end, 248 experimental results were collected from the literature, and a feed-forward ANN model was employed to predict the shear strength. To assess its feasibility, the ANNs were also modeled using the Particle Swarm Optimization, and Imperialist Competitive Algorithms. As a traditional technique, the multiple regression model was also employed. The shear design equations of ACI-318-2019 were also investigated and compared with Tabu Search Trained ANN model. The analysis of results suggests the superiority of Tabu Search Trained ANNs in comparison to other suggested models in literature and the ACI-318-2019 design code.
Highlights
The Artificial Neural Networks (ANNs) will be referred to by designation ANN-ALG nL (n1 − n2), where ALG designates the training algorithm used, nL signifies that the ANN has n hidden layers, and n1 and n2 designate the number of neurons in the first and second hidden layers, respectively
To assess the feasibility of using Tabu Search Training (TST) algorithm to train Artificial Neural Networks (ANNs) for predicting the shear strength of reinforced concrete specimens, 248 experimental test results were collected from published results
After training the artificial neural network model with the lowest Mean Squared Error (MSE) on test dataset was selected, a sensitivity analysis was conducted on the model, and to assess its accuracy, other ANN models trained using particle swarm optimization and imperialist competitive algorithm were employed. e analysis of the results suggests the following: (1) e trained ANN-TST 2L (9-5) model predicted the shear strength more accurately than other artificial neural networks. e mean squared error and model efficiency of this model on test data were 2217.08 and 0.9475, respectively
Summary
As one of the most lively fields of current research, Artificial Neural Networks (ANNs) offer valuable features and capabilities including learning and adapting to existing knowledge, generalization, parallel processing, and higher processing speed, and high error tolerance [21,22,23]. E bioinspired feedforward artificial neural network is an algorithm that consists of neurons organized in layers. Each neuron in a layer is connected to all neurons of the previous layers. E signals are transmitted between neurons through connection lines, and the weight of each connection shows its strength. The knowledge of the ANN is stored using these weights. To evaluate the output of each neuron, an activation function is applied to the sum of its weighted inputs (plus a bias value)
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