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.

中文翻译：

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基于香农熵的斜拉桥电缆损耗检测和定位方法

与任何土木结构或机制一样，车辆桥梁可能会遭受结构性损坏，如果不及时发现和纠正，可能会造成毁灭性的人员和经济损失。在这项工作中，提出了一种基于香农熵指数结合统计指标和模糊逻辑分类器的方法来检测和定位斜拉桥中的索损。香农熵指数用于表征与结构损伤相关的振动信号的变化，与损伤检测和损伤定位的统计指标相结合。另一方面，模糊逻辑分类器被用作模式识别算法，自动检测结构损伤。在这项研究中，分析了从 Rio Papaloapan 大桥（墨西哥韦拉克鲁斯）通过实验获得的振动数据。