Identifying Damages in girders of Bridges Using Square Time-Frequency Distribution and Neural Network

Document Type : Research Article


1 Department of Civil Eng.- University of Maragheh

2 Tehran North Branch, Islamic Azad University


The detection of damage to structures has received much attention, especially in recent years. In this research paper, a new method for detecting damage in concrete girders of bridge decks is presented. Ease of use, high accuracy, and reduction of monitoring costs are the requirements for the new method. In this research, signal processing tools and artificial intelligence were used to extract damage-sensitive features so that the presence of damage, its intensity, and its location can be determined with very high accuracy based solely on the vibration signals received from a sensor. The accuracy is about 99%, and the error percentage is less than 1. Based on the proposed method, firstly, using the time-frequency function, the response signals from the structure were processed. The neural network was trained, using the processed data. To evaluate, validate and ensure the performance of the proposed method, the numerical model of the concrete girder and the numerical model of the Shahid Madani bridge in Tabriz under normal and disturbed conditions were used. The results show the high diagnostic accuracy of this method and the lowest error rate in determining the condition of the structure and the location of the damaged element.


Main Subjects

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