Predicting Stress and Strain of FRP-Confined Square/Rectangular Columns Using Artificial Neural Networks

Abstract

4This study proposes the use of artificial neural networks (ANNs) to calculate the compressive 5 strength and strain of fiber reinforced polymer (FRP) confined square/rectangular columns. 6Modeling results have shown that the two proposed ANN models fit the testing data very 7 well. Specifically, the average absolute errors of the two proposed models are less than 5%. 8The ANNs were trained, validated, and tested on two databases. The first database contains 9 the experimental compressive strength results of 104 FRP confined rectangular concrete 10 columns. The second database consists of the experimental compressive strain of 69 FRP 11 confined square concrete columns. Furthermore, this study proposes a new potential approach 12 to generate a user-friendly equation from a trained ANN model. The proposed equations 13 estimate the compressive strength/strain with small error. As such the equations could be 14 easily used in engineering design instead of the "invisible" processes inside the ANN. 15

Neurocomputing in Civil Infrastructure

“…Gupta et al [98] presented a neural-expert system for prediction of concrete strength based on concrete mix design, size and shape of specimen, curing technique and period, among others. Pham and Hadi [99] predicted stress and strain in Fiber Reinforced Polymer (FRP)-con ned square and rectangular columns using ANN.…”

Prediction of the impact force on reinforced concrete beams from a drop weight

“…Therefore, in order to generate a simplified model to calculate impact force, the ''Tansig'' transfer function used in the previous section was replaced by the ''Purelin'' transfer function (equation (26)). Pham and Hadi (2014b) proposed a method that uses ANN to generate a user-friendly equation for predicting the compressive strength of FRP-confined concrete. This method is adopted in this study to build a simplified version of the proposed model.…”

“…The mathematical derivation of the method was presented in the study by Pham and Hadi (2014b); however, it is summarized herein to provide a brief background of the method. The architecture of the proposed model is modified to establish a simpler relationship between the input parameters and the impact force as shown in Figure 7.…”

Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods

“…Recently, a new category of models has been proposed based on soft computing methods, such as artificial neural networks, generic algorithms, and fuzzy logic. Models in this category can handle complex databases containing a large number of independent variables, identify the sensitivity of input parameters, and provide mathematical solutions between dependent and independent variables [16]. Pham and Hadi [16] proposed the utilization of neural networks to compute the strain and compressive strength of FRP-confined columns, and the results show agreement between proposed neural network models and experimental data.…”

“…Models in this category can handle complex databases containing a large number of independent variables, identify the sensitivity of input parameters, and provide mathematical solutions between dependent and independent variables [16]. Pham and Hadi [16] proposed the utilization of neural networks to compute the strain and compressive strength of FRP-confined columns, and the results show agreement between proposed neural network models and experimental data. Also, there are several studies related to design-oriented and analysis-oriented models [9,[17][18][19][20][21][22][23][24][25][26][27][28][29].…”