Author
Jose M. Machorro-Lopez
Bio: Jose M. Machorro-Lopez is an academic researcher. The author has contributed to research in topics: Structural health monitoring & Bridge (interpersonal). The author has an hindex of 1, co-authored 5 publications receiving 7 citations.
Papers
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TL;DR: A new methodology based on statistical features, Principal component analysis (PCA), and Mahalanobis distance (MD) for detecting and locating a cable loss in the Río Papaloapan bridge (RPB) using vibration signals is presented.
Abstract: Cable-stayed bridges are widely used all around the world. Unfortunately, during their service life, they are exposed to adverse conditions that may cause their deterioration and, consequently, the...
11 citations
07 Jun 2020
TL;DR: The presented results show that the proposed MUSIC method can make an accurate and reliable estimation of the condition and location of three specific damage conditions, i.e., loosened bolts, internal corrosion, and external corrosion.
Abstract: A new multiple signal classification (MUSIC)-based methodology is presented for detecting and locating multiple damage types in a truss-type structure subjected to dynamic excitations. The methodology is based mainly on two steps: in step 1, the MUSIC method is employed to obtain the pseudo-spectra of vibration signatures, healthy and damaged, to be used for damage detection. In step 2, a new damage index, based on the obtained pseudo-spectra, is proposed to measure the structure condition. Furthermore, the damage location is estimated according to the variation in the amplitudes of the estimated pseudo-spectra. The presented results show that the proposed methodology can make an accurate and reliable estimation of the condition and location of three specific damage conditions, i.e., loosened bolts, internal corrosion, and external corrosion.
11 citations
01 Feb 2021
TL;DR: In this paper, a wavelet-based method called the Wavelet Energy Accumulation Method (WEAM) is developed in order to detect, locate and quantify damage in vehicular bridges.
Abstract: Large civil structures such as bridges must be permanently monitored to ensure integrity and avoid collapses due to damage resulting in devastating human fatalities and economic losses. In this article, a wavelet-based method called the Wavelet Energy Accumulation Method (WEAM) is developed in order to detect, locate and quantify damage in vehicular bridges. The WEAM consists of measuring the vibration signals on different points along the bridge while a vehicle crosses it, then those signals and the corresponding ones of the healthy bridge are subtracted and the Continuous Wavelet Transform (CWT) is applied on both, the healthy and the subtracted signals, to obtain the corresponding diagrams, which provide a clue about where the damage is located; then, the border effects must be eliminated. Finally, the Wavelet Energy (WE) is obtained by calculating the area under the curve along the selected range of scale for each point of the bridge deck. The energy of a healthy bridge is low and flat, whereas for a damaged bridge there is a WE accumulation at the damage location. The Rio Papaloapan Bridge (RPB) is considered for this research and the results obtained numerically and experimentally are very promissory to apply this method and avoid accidents.
5 citations
TL;DR: During the last years, civil infrastructure has experienced an increasing development to satisfy the society’s demands such as communication, transportation, work and living spaces, among others.
Abstract: During the last years, civil infrastructure has experienced an increasing development to satisfy the society’s demands such as communication, transportation, work and living spaces, among others. I...
3 citations
TL;DR: In this article, the authors discuss the vulnerability of vehicular bridges to structural damages which can conduct to devastating human and economic losses if they are not detected and corrected on time....
Abstract: As with any civil structure or mechanism, vehicular bridges can suffer structural damages which can conduct to devastating human and economic losses if they are not detected and corrected on time. ...
1 citations
Cited by
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TL;DR: A comprehensive review of the recent progress that used signal processing techniques for vibration-based structural health monitoring (SHM) approaches is presented in this article , where the feature extraction process through the signal processing technique is the basic skeleton of this review.
42 citations
TL;DR: Experimental results show that FRDE can represent the complexity of signals and have the better separability; and FRDE-KNN has higher classification recognition rate than DE, permutation entropy and FDE with KNN, which can better classify the ship signals and gear fault signals.
23 citations
TL;DR: In this article , the authors combine phase-based motion estimation (PME) with the use of convolutional neural networks (CNNs) for feature extraction and classification of vibration signals that reveal structural damage.
11 citations
TL;DR: In this article , a review of the technical literature concerning variations in the vibration properties of civil structures under varying temperature conditions and damage identification methods for bridge structures is presented, considering data-based and model-based methods.
Abstract: In civil engineering structures, modal changes produced by environmental conditions, especially temperature, can be equivalent to or greater than the ones produced by damage. Therefore, it is necessary to distinguish the variations in structural properties caused by environmental changes from those caused by structural damages. In this paper, we present a review of the technical literature concerning variations in the vibration properties of civil structures under varying temperature conditions and damage identification methods for bridge structures. First, the literature on the effect of temperature on vibration properties is roughly divided into experimental and theoretical studies. According to the classification of theoretical research methods, the progress in research on the probability analysis method, the artificial intelligence method, and the optimization algorithm method in this field is reviewed. Based on the different methods of experimental research employed in this field, the experimental research is reviewed according to qualitative and quantitative analyses. Then, damage identification methods for bridge structures are reviewed, considering data-based and model-based methods. Finally, different research methods are summarized.
10 citations
TL;DR: Wang et al. as mentioned in this paper proposed a fitness-scaled adaptive genetic algorithm (FAGA) for MS recognition, which combines a standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling.
Abstract: Aim: Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord, leading to a wide range of potential symptoms. This method aims to propose a novel MS recognition method. Methods: First, the bior4.4 wavelet is used to extract multiscale coefficients. Second, three types of biorthogonal wavelet features are proposed and calculated. Third, fitness-scaled adaptive genetic algorithm (FAGA)-a combination of standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling-is harnessed as the optimization algorithm. Fourth, multiple-way data augmentation is utilized on the training set under the setting of 10 runs of 10-fold cross-validation. Our method is abbreviated as BWF-FAGA. Results: Our method achieves a sensitivity of 98.00 ± 0.95%, a specificity of 97.78 ± 0.95%, and an accuracy of 97.89 ± 0.94%. The area under the curve of our method is 0.9876. Conclusion: The results show that the proposed BWF-FAGA method is better than 10 state-of-the-art MS recognition methods, including eight artificial intelligence-based methods, and two deep learning-based methods.
8 citations