Abstract
Various methods have been proposed so far for damage detection of structures that use certain sensors to collect data. The sensitivity of different sensors to damage varies among the statistic parameters (features) extracted from signals obtained from them. In this paper, the concept of feature-sensor is proposed for the first time in this area. That is to say, for each effective sensor, a specific feature is designated. First, 17 features are selected, and each feature is assigned to all of the sensors. Using the Gini index, the most important feature-sensors are selected, and the accuracy of each feature-sensor is investigated using support vector machine (SVM). Next, feature selection is used to improve the accuracy of damage detection by using a combination of feature-sensors. The proposed method is validated by data from a real cable-stayed bridge. Results show that the method is able to distinguish damaged and healthy states with almost 98% accuracy.
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Afsharmovahed, M.H., Ghodrati Amiri, G. & Darvishan, E. A Novel Damage Detection Approach Based on Feature Extraction and Selection Using Machine Learning Without Signal Processing: A Case Study on the Tian** Yonghe Bridge. Iran J Sci Technol Trans Civ Eng 47, 3649–3661 (2023). https://doi.org/10.1007/s40996-023-01228-1
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DOI: https://doi.org/10.1007/s40996-023-01228-1