Estimating the compressive strength of rectangular fiber reinforced polymer–confined columns using multilayer perceptron, radial basis function, and support vector regression methods

Abstract

Recently, there has been a tendency to use machine learning (ML)–based methods, such as artificial neural networks (ANNs), for more accurate estimates. This paper investigates the effectiveness of three different machine learning methods including radial basis function neural network (RBNN), multi-layer perceptron (MLP), and support vector regression (SVR), for predicting the ultimate strength of square and rectangular columns confined by various FRP sheets. So far, in the previous study, several experiments have been conducted on concrete columns confined by fiber reinforced polymer (FRP) sheets with the results suggesting that the use of FRP sheets enhances the compressive strength of concrete columns effectively. Also, a wide range of experimental data (including 463 specimens) has been collected in this study for square and rectangular columns, confined by various FRP sheets. The comparison of ML-derived results with the experimental findings, which were in a very good agreement, demonstrated the ability of ML to estimate the compressive strength of concrete confined by FRP; the correlation coefficient (R2) for MLP, RBFNN, and SVR methods was equal to 0.97, 0.97, and 0.90, respectively. Similar accuracy was obtained by MLP and RBFNN, and they provided better estimates for determining the compressive strength of concrete confined by FRP. Also, the results showed that the difference between statistical indicators for training and testing specimens in the RBFNN method was greater than the MLP method, and this difference indicated the poor performance of RBFNN.

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

“…In the MLP network, data flows from the input to the output layer. The hidden layer, placed between the input and output layers, is the core of the MLP, which processes the input information and transfers it to the output layer [53]. The neurons are the processing elements in the MLP, and the neurons in each layer are connected to every neuron in the next layer.…”

Developing a support vector regression model via optimization algorithms to appraise the hardness properties of high‐performance concrete

“…have used machine learning approaches including multilayer perceptron (MLP), radial basis function neural network (RBNN), and SVM to estimate the hardness features of concrete samples. Utilizing experimental 463 samples data in mentioned models, the R 2 correlation coefficients were obtained 0.970, 0.973, and 0.91 for MLP, RBF, and SVR methods 8 . Saha et al .…”

“…Utilizing experimental 463 samples data in mentioned models, the R 2 correlation coefficients were obtained 0.970, 0.973, and 0.91 for MLP, RBF, and SVR methods. 8 Saha et al used SVM way in the prediction of hardness features of selfcompacting concrete (SCC) were used to develop the SVR via two different kernel functions of radial basis function (RBF) and the exponential types of it (ERBF). By collecting 115 SCC mixtures their ingredients including FLA, water-powder ratio, superplasticiser, fine aggregate, coarse aggregate, and binder content, RBF was successful in both hybrid models to appraise CS values with a correlation coefficient of 0.977.…”

“…[4][5][6] An important technique in the production of HPC is to make HPC densely. 7,8 Therefore, the crucial norms which distinguish producing HPC from other concrete types are: (a) utilizing smaller aggregate particles, (b) mixing additional cement-based matters such as silica-fume (SF), and (c) using superplasticizer as water-reducing agents to reduce ratio of water/binder. [9][10][11] For composing HPC mixtures, selection and control of the components and their ratios are vital factors to consider.…”

“…These improvements in mechanical features have led HPC to be employed widely in constructing applications, essentially in road construction, high‐rise towers, tunnels, and long bridges 4–6 . An important technique in the production of HPC is to make HPC densely 7,8 . Therefore, the crucial norms which distinguish producing HPC from other concrete types are: (a) utilizing smaller aggregate particles, (b) mixing additional cement‐based matters such as silica‐fume (SF), and (c) using superplasticizer as water‐reducing agents to reduce ratio of water/binder 9–11 .…”