A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prestressing wire breakage monitoring using sound event detection
AbstractDetecting prestressed wire breakage in concrete bridges is essential for ensuring safety and longevity and preventing catastrophic failures. This study proposes a novel approach for wire breakage detection using Mel‐frequency cepstral coefficients (MFCCs) and back‐propagation neural network (BPNN). Experimental data from two bridges in Italy were acquired to train and test the models. To overcome the limited availability of real‐world training data, data augmentation techniques were employed to increase the data set size, enhancing the capability of the models and preventing over‐fitting problems. The proposed method uses MFCCs to extract features from acoustic emission signals produced by wire breakage, which are then classified by the BPNN. The results show that the proposed method can detect and classify sound events effectively, demonstrating the promising potential of BPNN for real‐time monitoring and diagnosis of bridges. The significance of this work lies in its contribution to improving bridge safety and preventing catastrophic failures. The combination of MFCCs and BPNN offers a new approach to wire breakage detection, while the use of real‐world data and data augmentation techniques are significant contributions to overcoming the limited availability of training data. The proposed method has the potential to be a generalized and robust model for real‐time monitoring of bridges, ultimately leading to safer and longer‐lasting infrastructure.
Prestressing wire breakage monitoring using sound event detection
AbstractDetecting prestressed wire breakage in concrete bridges is essential for ensuring safety and longevity and preventing catastrophic failures. This study proposes a novel approach for wire breakage detection using Mel‐frequency cepstral coefficients (MFCCs) and back‐propagation neural network (BPNN). Experimental data from two bridges in Italy were acquired to train and test the models. To overcome the limited availability of real‐world training data, data augmentation techniques were employed to increase the data set size, enhancing the capability of the models and preventing over‐fitting problems. The proposed method uses MFCCs to extract features from acoustic emission signals produced by wire breakage, which are then classified by the BPNN. The results show that the proposed method can detect and classify sound events effectively, demonstrating the promising potential of BPNN for real‐time monitoring and diagnosis of bridges. The significance of this work lies in its contribution to improving bridge safety and preventing catastrophic failures. The combination of MFCCs and BPNN offers a new approach to wire breakage detection, while the use of real‐world data and data augmentation techniques are significant contributions to overcoming the limited availability of training data. The proposed method has the potential to be a generalized and robust model for real‐time monitoring of bridges, ultimately leading to safer and longer‐lasting infrastructure.
Prestressing wire breakage monitoring using sound event detection
Computer aided Civil Eng
Farhadi, Sasan (author) / Corrado, Mauro (author) / Borla, Oscar (author) / Ventura, Giulio (author)
Computer-Aided Civil and Infrastructure Engineering ; 39 ; 186-202
2024-01-01
Article (Journal)
Electronic Resource
English
Automated acoustic event‐based monitoring of prestressing tendons breakage in concrete bridges
| Wiley | 2024
Automated acoustic event-based monitoring of prestressing tendons breakage in concrete bridges
| BASE | 2024