# Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members

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## Abstract

**:**

## 1. Introduction

## 2. Dataset of Damaged and Undamaged RC Elements

## 3. Hybrid Models’ Details

## 4. Results

## 5. Comparative Study

## 6. Conclusions

- WSNs were great for extracting domain features from image data.
- In the training stage, all models, apart from the WSN-SVM algorithm, received the highest score (100%) across all criteria. Only the WSN-SVM technique had a 99.8% accuracy rate and had one error for SD type failure.
- The good performance of a model in the training phase does not mean it will have a good performance on new data.
- The WSN-SVM algorithm classified the damage the most accurately throughout the testing phase. With a value of 99.1 percent during testing, it had the hybrid models’ highest accuracy. The differences in the performance evaluation criteria for this model in the two phases of training and testing were very close.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 8.**Performance of hybrid models (training phase). (

**a**) WSN-ANN algorithm. (

**b**) WSN-BT algorithm. (

**c**) WSN-RSE algorithm. (

**d**) WSN-SVM algorithm.

**Figure 9.**Performance of hybrid models (testing phase). (

**a**) WSN-ANN algorithm. (

**b**) WSN-BT algorithm. (

**c**) WSN-RSE algorithm. (

**d**) WSN-SVM algorithm.

**Figure 10.**Learning curves. (

**a**) WSN-ANN algorithm. (

**b**) WSN-BT algorithm. (

**c**) WSN-RSE algorithm. (

**d**) WSN-SVM algorithm.

State | Undamaged | Flexural Damage | Flexural-Shear (Combined) Damage | Shear Damage | Total |
---|---|---|---|---|---|

Symbol | UN | FD | CD | SD | - |

Sample number | 1813 | 522 | 1325 | 925 | 4585 |

Models | Accuracy (%) |
---|---|

WSN-ANN | 0.943 |

WSN-BT | 0.953 |

WSN-RSE | 0.953 |

WSN-SVM | 0.991 |

Models | Failure Mode | ||||
---|---|---|---|---|---|

CD | FD | UD | SD | ||

Recall (%) | WAE-DE | 96.17 | 91.57 | 97.3 | 87.73 |

WSN-SVM | 99.1 | 98.6 | 99.6 | 98.5 | |

Precision (%) | WAE-DE | 93.12 | 96.7 | 91.97 | 96.62 |

WSN-SVM | 99.5 | 97.3 | 99.3 | 99.2 |

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**MDPI and ACS Style**

Barkhordari, M.S.; Barkhordari, M.M.; Armaghani, D.J.; Rashid, A.S.A.; Ulrikh, D.V.
Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members. *Sustainability* **2022**, *14*, 12041.
https://doi.org/10.3390/su141912041

**AMA Style**

Barkhordari MS, Barkhordari MM, Armaghani DJ, Rashid ASA, Ulrikh DV.
Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members. *Sustainability*. 2022; 14(19):12041.
https://doi.org/10.3390/su141912041

**Chicago/Turabian Style**

Barkhordari, Mohammad Sadegh, Mohammad Mahdi Barkhordari, Danial Jahed Armaghani, Ahmad Safuan A. Rashid, and Dmitrii Vladimirovich Ulrikh.
2022. "Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members" *Sustainability* 14, no. 19: 12041.
https://doi.org/10.3390/su141912041