Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

Barkhordari, Mohammad Sadegh; Feng, De‐Cheng; Tehranizadeh, Mohsen

doi:10.3311/ppci.19323

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…Meanwhile, the precision and recall of SSM-SVM, ISE, and WAE-DE are greater than 91% for almost all damage classes. While the recognition of FS damage is challenging in other research studies [46,47], the WAE-DE and SSM-SVM model achieved a near 93% recall and 96% precision in identifying the flexure-shear failure type in the testing dataset. Nevertheless, it seems that the prediction of the FS damage is challenging for the other examined models.…”

Section: Resultsmentioning

confidence: 86%

Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

Barkhordari¹,

Armaghani²

2023

Computer Modeling in Engineering &Amp; Sciences

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The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is required. Ensemble learning algorithms for identifying structural damage are evaluated in this article, which use deep convolutional neural networks, including simple averaging, integrated stacking, separate stacking, and hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification is carried out on three types of damage. The proposed algorithms are used to analyze the damage of 4585 structural images. The effectiveness of the ensemble learning techniques is evaluated using the confusion matrix. For the testing dataset, the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model (WAE-DE) in distinguishing damage types as flexural, shear, combined, or undamaged.

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Section: Resultsmentioning

confidence: 86%

Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

Barkhordari¹,

Armaghani²

2023

Computer Modeling in Engineering &Amp; Sciences

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“…As mentioned above, shear equations/models of traditional R.C. members can be employed to evaluate the shear contribution of the concrete encasement; some emerging tool, e.g., deep learning, needs to be involved in the future to predict the shear strength of SRC members [24].…”

Section: Comparison Between Tested and Calculated Resultsmentioning

confidence: 99%

Compatible Truss-Arch Model for Predicting the Shear Strength of Steel Shape-Reinforced Concrete (SRC) Beams

Xue

Liu

et al. 2023

Buildings

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A steel shape-reinforced concrete (SRC) beam, in which a steel profile is encased in an R.C. section, is an essential configuration of steel-concrete composite members. Nevertheless, the precise estimation of shear strength for SRC elements is currently being explored due to the challenges associated with incorporating steel-concrete interaction. This paper establishes a compatible truss-arch model to simulate the shear behavior of SRC beams and predict their maximum shear strength. In the established model, the shear contribution of the R.C. encasement is evaluated using the traditional truss-arch model, and a stress decomposition based on von Mises yielding criterion and strain compatibility is conducted within the steel shape to decouple its shear contribution. Finally, the validity of the proposed model is confirmed using a comprehensive database. The comparison between the experimental and calculated results demonstrates that the established model can effectively and reliably calculate the SRC beams’ shear strength.

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“…In recent years, the use of deep learning models for bearing fault detection has become a popular research direction [ 12 ]. Deep learning algorithms have grown increasingly attractive in such rapid applications because of their increased reliabilities and simplicity compared with traditional methods [ 13 ]. Furthermore, deep learning models are continuously being optimized and innovated, and datasets are being continuously improved.…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Diagnosis Method Based on a Residual Network and a Gated Recurrent Unit under Time-Varying Working Conditions

Zheng

Zhang

et al. 2023

Sensors

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The diagnosis of bearing faults is an important guarantee for the healthy operation of mechanical equipment. Due to the time-varying working conditions of mechanical equipment, it is necessary to achieve bearing fault diagnosis under time-varying working conditions. However, the superposition of the two-dimensional working conditions of speed and acceleration brings great difficulties to diagnosis via data-driven models. The long short-term memory (LSTM) model based on the infinitesimal method is an effective method to solve this problem, but its performance still has certain limitations. On this basis, this article proposes a model for fault diagnosis under time-varying operating conditions that combines a residual network model (ResNet) and a gate recurrent unit (model) (GRU). Firstly, the samples were segmented, and feature extraction was performed using ResNet. We then used GRU to process the information. Finally, the classification results were output through the output network. This model could ignore the influence of acceleration and achieve high fault diagnosis accuracy under time-varying working conditions. In addition, we used t-SNE to reduce the dimensionality of the features and analyzed the role of each layer in the model. Experiments showed that this method had a better performance compared with existing bearing fault diagnosis methods.

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Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

Cited by 9 publications

References 29 publications

Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

Compatible Truss-Arch Model for Predicting the Shear Strength of Steel Shape-Reinforced Concrete (SRC) Beams

A Bearing Fault Diagnosis Method Based on a Residual Network and a Gated Recurrent Unit under Time-Varying Working Conditions

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