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

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.

Automated summary

A methodology combining Shannon entropy index, statistical indexes, and fuzzy logic classifier is proposed for detecting and locating cable losses in cable-stayed bridges. Experimental results show an effectiveness of 93.3% in damage detection and 100% in damage location using this method.