Civil structures are always considered one of the most valuable properties of each country. Many factors can lead to local damages in different parts of structures during their operational life. These damages are reflected in the vibration responses of structures. This research aims to detect the existence and determine the location of damage in a truss bridge under moving load using an artificial neural network and experimental wavelet transform. For this purpose, a two-dimensional truss bridge was built in the laboratory to investigate this research's objectives. Earlier experimental studies in damage detection were subjected to excitations such as impact loads and electrodynamic shakers. Since the appearance of damage effects in the vibration responses of the structure mainly depends on the applied location of the impact load, a moving load that crosses the entire length of the bridge can be used as input excitation to detect the presence and location of damages for which there is no available data. After measuring the vibration responses of the bridge, 17 time-domain features were extracted from the raw signals, which were used to detect the presence of damage. Although feature extraction is applied to raw signals, signal processing stage was not eliminated for damage localization. By processing the response signals of healthy and damaged state of the bridge using experimental wavelet transform, these signals were decomposed into different modes and 5 non-parametric damage-sensitive features such as Shannon and Tsallis entropies, Root Mean Square (RMS), Shape Factor and kurtosis which are all based on statistical parameters in addition to energy, were extracted. Finally, these damage-sensitive features were presented as input to the neural network whereas the state of the bridge (healthy or damaged) was considered as its targets. The obtained results showed that the proposed method is able to effectively detect the presence and the location of the damage in the truss bridge.
Shahmohammadi, S., & Mahmoudi, M. (2024). Experimental study on damage detection of a truss bridge under moving load using artificial neural network and empirical wavelet transform. Sharif Journal of Civil Engineering, (), -. doi: 10.24200/j30.2023.62521.3229
MLA
Siavash Shahmohammadi; Mussa Mahmoudi. "Experimental study on damage detection of a truss bridge under moving load using artificial neural network and empirical wavelet transform". Sharif Journal of Civil Engineering, , , 2024, -. doi: 10.24200/j30.2023.62521.3229
HARVARD
Shahmohammadi, S., Mahmoudi, M. (2024). 'Experimental study on damage detection of a truss bridge under moving load using artificial neural network and empirical wavelet transform', Sharif Journal of Civil Engineering, (), pp. -. doi: 10.24200/j30.2023.62521.3229
VANCOUVER
Shahmohammadi, S., Mahmoudi, M. Experimental study on damage detection of a truss bridge under moving load using artificial neural network and empirical wavelet transform. Sharif Journal of Civil Engineering, 2024; (): -. doi: 10.24200/j30.2023.62521.3229
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