Empirical Wavelet Transform-based Detection of Anomalies in ULF Geomagnetic Signals Associated to Seismic Events with a Fuzzy Logic-based System for Automatic Diagnosis

Owing to the relevance and severity of damages caused by earthquakes (EQs), the development and application of new methods for seismic activity detection that offer an efficient and reliable diagnosis in terms of processing and performance are still demanding tasks. In this work, the application of the Empirical Wavelet Transform (EWT) for seismic detection in ultra-low-frequency (ULF) geomagnetic signals is presented. For this, several ULF signals associated to seismic activities and random calm periods are analysed. These signals have been obtained through a tri-axial fluxgate magnetometer at the Juriquilla station localized in Queretaro, Mexico, longitude -100.45° N and latitude 20.70°E. In order to show the advantages of the proposal, a comparison with the discrete wavelet transform (DWT) is presented. The results shown a better detection capability of seismic signals before, during, and after the main shock than the ones obtained by the DWT, which makes the proposal a more suitable and reliable tool for this task. Finally, a fuzzy logic (FL)-based system for automatic diagnosis using the variance of the EWT outputs for the tri-axial fluxgate magnetometer signals is also proposed

CBR Predictive Models for Granular Bases Using Physical and Structural Properties

The California bearing ratio (CBR) test evaluates the structure of the layers of pavements. Such a test is laborious, time-consuming, and its results are generally affected by sample disturbance and tests conditions. The main objective of this research was to build a numerical model for the prediction of CBR tests that might substitute laboratory tests. The model was based on structural and physical parameters of granular bases. Four different materials from the central region (Querétaro) and north (Mexicali) of Mexico were used for the experimental work. Using the above-mentioned materials, 36 samples were fabricated, and six of them were used for the evaluation of the model presented in this research. Numerical and experimental comparisons demonstrated the adequacy of the model to predict the result of CBR tests from soil parameters

EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure

Truss-type designs are widely used in civil structures. Despite the fact that they are robust and reliable structures, different kinds of damage can appear. In order to avoid human and economic losses, the development and application of damage-detection methodologies are paramount. In this work, a methodology based on the empirical mode decomposition (EMD) method and the Shannon Entropy Index (SEI) to detect incipient damages associated with corrosion in a 3D 9-bay truss-type bridge is presented. As different EMD methods are presented in literature, the most representative methods are investigated in order to evaluate their performance for this task. To this end, the vibration signals generated in the truss-type bridge at different conditions are analyzed. For the damage condition, four severity levels of simulated corrosion (1 mm, 3 mm, 5 mm, and 8 mm of diameter reduction) generated into the elements of truss-type bridge are considered. Results demonstrate the effectiveness of the proposal in terms of detecting corrosion in its very early stage (1 mm of reduction in the element)