Effective Damage Identification (DI) plays a critical role in protecting structures against local or global failures caused by hazards. Real-time DI provides instant damage data and increases the safety and serviceability of civil structures. Real-time DI helps to understand the structure's behavior during extreme events that may be unknown at the design stage. This field needs innovative solutions for training supervised machine learning classifiers in the absence of measured damaged data. This research proposes an unconventional deep learning algorithm for vibration-based DI. The proposed real-time data-driven DI methodology does not require any manual feature extraction and uses Artificial Neural Networks (ANNs) to identify the presence and location of damage in discrete structural systems. The input is the response signals measured through sensors (no model-based input information required). A dropout technique regularizes the network and avoids co-adaptation in hidden layers. The neural network is optimized through 10-fold cross-validation. The proposed method's effectiveness in identifying the presence and location of damages is studied using a 4-story 2D structure subjected to artificial accelerograms. The recorded response signals create the feature space in the dataset. The lateral stiffness of columns is reduced randomly by different percentages resembling different damage severities. Considering the validation dataset results, the accuracy of the damage detection task varies from 84 to 99% for different damage severities, and accuracy for the localization task ranges from 78-98%. The results show the promising performance of ANNs for real-time DI and pave the way for training the classifiers using real-life data from undamaged structures and simulate data from damage scenarios.
While dealing with structures equipped with operating mechanical devices, keeping the machinery-induced vibrations below the acceptable limits is of enormous importance. The fundamental step to controlling undesirable vibrations in such structures is to localize the vibration source. The accuracy of locating the source of vibration using different methods, e.g., Time Difference of Arrival (TDOA) or Steered Response Power (SRP) method, depends on accurate estimation of the propagation speed. The propagation speed is a function of vibration frequency. The objective of this study is to investigate a nonlinear regression model to obtain the relationship between Wave Propagation Speed (WPS) and the vibration frequency on a concrete floor. The development of this relationship is based on a series of experiments on a concrete floor in a building using a shaker as a vibration exciter, and four accelerometers to record vertical vibration. First, the shaker generates sinusoid forces with a specific frequency and the accelerometers, configured collinearly, record acceleration measurements. Then, the WPS is estimated using cross-correlation to measure the time difference of arrival between pairs of accelerometers. This process is repeated for a range of frequencies resulting in a dataset that includes the vibration frequency as independent and the WPS as dependent variables. The relationship between speed and frequency is then optimally estimated using a nonlinear regression model.
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