A Shannon Entropy-Based Methodology to Detect and Locate Cables Loss in a Cable-Stayed Bridge

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. In this work, a methodology based on the Shannon entropy index combined with statistical indexes and a fuzzy logic classifier to detect and locate a cable loss in cable-stayed bridges is proposed. Shannon entropy index is used to characterize the changes in the vibration signals associated with structural damage, which are integrated with statistical indexes for damage detection and damage location. On the other hand, the fuzzy logic classifier is used as a pattern recognition algorithm to detect structural damage automatically. For this study, the vibration data acquired experimentally from the Rio Papaloapan Bridge (Veracruz, Mexico) are analyzed. Results demonstrate the usefulness of the proposed method since 93.3% of effectiveness in the damage detection is obtained with a 100% of effectiveness in its location.

Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification

“…When p(x = 1) = 0.5, all possible outcomes are equally probable, meaning the result is not very predictable, and that the entropy is maximum. In the last decade, the Shannon entropy has been applied for different purposes, including the evaluation of the health condition of structures [44,45]. The Shannon entropy measures the uncertainty in a random variable [57].…”

“…The Shannon entropy measures the uncertainty in a random variable [57]. Therefore, this can be a reliable indicator to identify damage in a civil construction, since the vibration signals change according to the damage level, consequently generating a variation in the Shannon entropy magnitude [44,45]. Therefore, since the Shannon entropy is associated with chaos, this helps to determine in which parts of a signal there is more or less chaos, associating high levels of chaos or entropy with an important phenomena or critical situations in the signals.…”

“…Therefore, since the Shannon entropy is associated with chaos, this helps to determine in which parts of a signal there is more or less chaos, associating high levels of chaos or entropy with an important phenomena or critical situations in the signals. In the last decade, the Shannon entropy has been applied for different purposes, including the evaluation of the health condition of structures [44,45]. The Shannon entropy measures the uncertainty in a random variable [57].…”

Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor

“…Decreased accuracy with increasing distance [24][25][26] Vibration-based method Remote sensing Complex data analysis [27][28][29][30][31][32][33] The proposed method Less data processing, real-time responsiveness, and lightweight for robots to carry Applied to small-sized cables currently…”

“…Zarbaf et al [29] proposed a cable tension estimation framework based on an artificial neural network using parameters such as cable length, mass per unit length, axial stiffness, bending stiffness, cable tension, and corresponding natural frequencies obtained from a comprehensive cable model, enabling accurate estimation of cable tension. Machorro-Lopez et al [30] used the Shannon entropy index to characterize cable vibration signals and employed a fuzzy logic classifier for pattern recognition to detect and locate cable structural damage affected by environmental vibrations. Peng et al [31] proposed a long-term condition assessment method for monitoring stay cables based on the concept of the influence surface and the linear relationship between the matching cable tensions of two adjacent cables.…”