Soft Computing-based Approach on Capacity Prediction of FRP Strengthened RC Joints

Ilkhani, M.H.; Naderpour, Hosein; Kheyroddin, Ali

doi:10.24200/sci.2018.20177

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…The efficiency in terms of structural capacity of RC joints has been experimentally demonstrated [51,52]. Strengthening based on fiber-reinforced polymer (FRP) or fiber-reinforced cementitious matrix (FRCM) [53][54][55][56][57][58][59] can be used to improve the structural capacity of RC joints. The best strategy depends significantly on the mechanical performance of the concrete support [60,61].…”

Section: Introductionmentioning

confidence: 99%

Unified Simplified Capacity Model for Beam-Column Joints into RC Moment Resisting Frame

Ramaglia

Lignola²,

Fabbrocino³

et al. 2022

Applied Sciences

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Seismic vulnerability of reinforced concrete (RC) buildings is strongly influenced by beam-column joints. Horizontal and vertical structural members converge in beam-column joints, as extremely delimited areas where the internal forces in concrete core and reinforcing bars have elevated stress gradients. In order to fully understand the seismic behavior of RC buildings and the related failures of T- and X-shaped beam-column joints (external corner and internal-positioned), an analytical model of RC joint behavior is proposed in a unified and simplified way. The equilibrium equations of cracked joint portions allow the assessment of internal stresses’ evolution at increasing values of column shear forces. In this way, the strength hierarchy is evaluated in terms of capacity for the different potential failure modes. This can drive, as a useful tool for designers, the design of new efficient structures or the assessment of existing ones to occur, with subsequent interventions to move an initial undesired failure mode to a more suitable one. Nearly 500 experimental results of tests available in the literature are compared with the analytical predictions of the proposed model.

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Section: Introductionmentioning

confidence: 99%

Unified Simplified Capacity Model for Beam-Column Joints into RC Moment Resisting Frame

Ramaglia

Lignola²,

Fabbrocino³

et al. 2022

Applied Sciences

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“…Artificial intelligence and soft computing methods, more commonly known as machine learning methods, are widely used nowadays in many fields, especially in civil engineering, as effective methods to link complex experimental data. [16][17][18][19][20][21][22][23] Thus, they are suitable alternatives for solving various problems, by minimizing the difference between actual and predicted results. Ilkhani et al 16 provided a relationship for estimating the shear strength of RC beamcolumn joints strengthened by FRP using neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…[16][17][18][19][20][21][22][23] Thus, they are suitable alternatives for solving various problems, by minimizing the difference between actual and predicted results. Ilkhani et al 16 provided a relationship for estimating the shear strength of RC beamcolumn joints strengthened by FRP using neural networks. In 2019, Rezaie-Balf, 17 by collecting 228 experimental case studies of the scour depth downstream of sluice gates with an apron and using multivariate adaptive regression splines (MARS), proposed a relationship for the scour depth.…”

Section: Introductionmentioning

confidence: 99%

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

Moodi

Ghasemi

Mousavi

2021

Journal of Reinforced Plastics and Composites

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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.

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Soft Computing-based Approach on Capacity Prediction of FRP Strengthened RC Joints

Cited by 2 publications

References 28 publications

Unified Simplified Capacity Model for Beam-Column Joints into RC Moment Resisting Frame

Unified Simplified Capacity Model for Beam-Column Joints into RC Moment Resisting Frame

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

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