Zhicheng Liu¹, Long Zhao^1,*, Guanru Wen¹, Peng Yuan², Qiu Jin¹

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 541-555, 2023, DOI:10.32604/sdhm.2023.029760

Abstract The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method. Then the vibration signal… More > Graphic Abstract

Sidra Naseem¹, Kashif Javed¹, Muhammad Jawad Khan¹, Saddaf Rubab², Muhammad Attique Khan³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 471-486, 2021, DOI:10.32604/cmc.2021.018239

Abstract Electroencephalography is a common clinical procedure to record brain signals generated by human activity. EEGs are useful in Brain controlled interfaces and other intelligent Neuroscience applications, but manual analysis of these brainwaves is complicated and time-consuming even for the experts of neuroscience. Various EEG analysis and classification techniques have been proposed to address this problem however, the conventional classification methods require identification and learning of specific EEG characteristics beforehand. Deep learning models can learn features from data without having in depth knowledge of data and prior feature identification. One of the great implementations of deep learning is Convolutional Neural Network… More >

Ahmed Silik^1,2, Mohammad Noori^3,*, Wael A. Altabey^1,4, Ramin Ghiasi¹, Zhishen Wu¹

Structural Durability & Health Monitoring, Vol.15, No.1, pp. 1-22, 2021, DOI:10.32604/sdhm.2021.012751

Abstract A critical problem facing data collection in structural health monitoring, for instance via sensor networks, is how to extract the main components and useful features for damage detection. A structural dynamic measurement is more often a complex time-varying process and therefore, is prone to dynamic changes in time-frequency contents. To extract the signal components and capture the useful features associated with damage from such non-stationary signals, a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required. Wavelet analyses have proven to be a viable and effective tool in this… More >

Zheng Li^1,2, Wei Zhang¹, Kezhuang Gong¹

CMC-Computers, Materials & Continua, Vol.9, No.3, pp. 209-228, 2009, DOI:10.3970/cmc.2009.009.209

Abstract In this paper, continuous wavelet transform has been applied to inclusion detection in cantilever beams. By means of FEM, a cantilever beam with an inclusion is subjected to an impact on its free end, and its stress wave propagation process is calculated. Here, two kinds of inclusions which are distinct in material behavior have been discussed. And we change the inclusion's sizes in the beam and set it in three different positions to simulate some complicated situations. For soft inclusion, the results show that the arrival times of incident and reflective wave are distinguishable by performing Gabor wavelet transform and… More >