Prediction of shear capacity of RC beams strengthened with FRCM composite using hybrid ANN-PSO model

Nguyen, Trong-Ha; Tran, Ngoc-Long; Phan, Van-Tien; Nguyen, Duy-Duan

doi:10.1016/j.cscm.2023.e02183

Cited by 11 publications

(4 citation statements)

References 60 publications

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“…Researchers are now turning to machine learning approaches to accurately forecast the results of complex engineering challenges. It is used in a variety of fields, such as structural engineering, − construction management project decision-making, , mechanical engineering problem-solving, and several concrete technology-related issues. However, there are many examples of it being used in structural dynamics research.…”

Section: Introductionmentioning

confidence: 99%

Inclined Curved Crack Composite Beam: Experimental and Computational Analysis with Recycled Aluminum Composite Materials and Comparison with an Artificial Neural Network

Ravichandran,

Mohanty,

Asiri

et al. 2024

ACS Omega

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This research focuses on an inclined curved crack model for a recycled aluminum composite beam at various crack depths and locations. The inclined curved crack equation of the motion, by governing a free vibration curved beam with a different depth of crack, is solved computationally via the differential quadrature method (DQM) and experimentally; additionally, the result of the natural frequency has been compared with various depths of curvature. For the first four modes of cracked beams, the computational method’s output is used to determine the natural frequencies associated with mode shapes. The outcomes of the computational results suggested a structural health monitoring system to detect deterioration in composite structures when modal parameters have changed. An experimental set of results was validated using MATLAB2019a, and the outcomes were compared with an artificial neural network.

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Section: Introductionmentioning

confidence: 99%

Inclined Curved Crack Composite Beam: Experimental and Computational Analysis with Recycled Aluminum Composite Materials and Comparison with an Artificial Neural Network

Ravichandran,

Mohanty,

Asiri

et al. 2024

ACS Omega

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“…Bayari et al [ 37 ] employed the ANN-PSO for the estimation and assessment of collapse danger and the determination of structural reliability. Nguyen et al [ 38 ] have developed ANN-PSO to predict the shear strength of reinforced concrete (RC) beams. The mentioned studies illustrated that the hybridization of ANN with PSO improves convergence and enhances the accuracy of the quality of results for modeling concrete structure elements.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of irregularity RC structure based on ANN-PSO

Zhang

2024

Heliyon

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“…It can also deal with nonlinear problems and is widely used in various industries. Currently, ANNs have been used in many fields to make predictions, such as predicting the annual consumption of natural gas [16], the wear rates of Al-MnO2 nanocomposites [17], the shear strengths of beams [18], the energy consumption of heating stations [19], the underground temperature [20], and the energy consumption of typical households [21]. Additionally, there are various applications related to forecasting water usage.…”

Section: Introductionmentioning

confidence: 99%

ANN-LSTM-A Water Consumption Prediction Based on Attention Mechanism Enhancement

Zhou,

Meng,

2024

Energies

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To reduce the energy consumption of domestic hot water (DHW) production, it is necessary to reasonably select a water supply plan through early predictions of DHW consumption to optimize energy consumption. However, the fluctuations and intermittence of DHW consumption bring great challenges to the prediction of water consumption. In this paper, an ANN-LSTM-A water quantity prediction model based on attention mechanism (AM) enhancement is improved. The model includes an input layer, an AM layer, a hidden layer, and an output layer. Based on the combination of artificial neural network (ANN) and long short-term memory (LSTM) models, an AM is incorporated to address the issue of the traditional ANN model having difficulty capturing the long-term dependencies, such as lags and trends in time series, to improve the accuracy of the DHW consumption prediction. Through comparative experiments, it was found that the root mean square error of the ANN-LSTM-A model was 15.4%, 13.2%, and 13.2% lower than those of the ANN, LSTM, and ANN-LSTM models, respectively. The corresponding mean absolute error was 17.9%, 11.5%, and 8% lower than those of the ANN, LSTM, and ANN-LSTM models, respectively. The results showed that the proposed ANN-LSTM-A model yielded better performances in predicting DHW consumption than the ANN, LSTM, and ANN-LSTM models. This work provides an effective reference for the reasonable selection of the water supply plan and optimization of energy consumption.

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Prediction of shear capacity of RC beams strengthened with FRCM composite using hybrid ANN-PSO model

Cited by 11 publications

References 60 publications

Inclined Curved Crack Composite Beam: Experimental and Computational Analysis with Recycled Aluminum Composite Materials and Comparison with an Artificial Neural Network

Inclined Curved Crack Composite Beam: Experimental and Computational Analysis with Recycled Aluminum Composite Materials and Comparison with an Artificial Neural Network

Design optimization of irregularity RC structure based on ANN-PSO

ANN-LSTM-A Water Consumption Prediction Based on Attention Mechanism Enhancement

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