[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123358-en":3,"doc-seo-123358-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123358,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Research on Damage Detection of Civil Structures Based on Machine Learning of Multiple Vegetation Index Time Series","A damage identification approach for civil structures is developed by combining modal parameters with machine learning feature construction. Structural dynamic response analysis uses natural frequency characteristics to form identification quantities for each process. For damaged regions, the median curve of multiple vegetation index time series is extracted after 5G filtering and compared with actual crop growth curves to build a vegetation-index monitoring model. The best threshold is selected as 10, and the method verifies accurate pixel-level surface damage detection for concrete cracks, spalling, and exposed steel bars. Iteration time is 0.18 hours.","ISSN 1330-3651 (Print), ISSN 1848-6339 (Online) [https://doi.org/10.17559/TV-20240104001243](https://doi.org/10.17559/TV-20240104001243)  \nOriginal scientific paper  \nResearch on Damage Detection of Civil Structures Based on Machine Learning of Multiple  \nVegetation Index Time Series  \nJianling TAN*, Xuejing ZHANG, Dan LI, Hanzheng SUN  \nAbstract: On the basis of analyzing the natural frequency of the structure, the identification quantity of each process is constructed with modal parameters and input into the machine learning as characteristic parameters to realize the damage identification. By extracting the median curve of vegetation index time series after 5G filtering in the damaged area of typical civil structures, and comparing it with the actual growth curve of crops in the area, the vegetation index time series monitoring model was constructed, and 10 was selected as the best threshold. The accuracy of the result is verified, and the iteration time is 0.18 hours. A damage detection method based on machine learning is proposed. Good prediction results are obtained for three common surface damage of concrete cracks, spalling and exposed steel bars, which verify the ability of this method to accurately identify and detect structural surface damage at pixel level.  \nKeywords: civil structure damage detection; machine learning; multiple vegetation index time series; structural damage identification  \n1 INTRODUCTION  \nFor engineering structures, the dynamic response of structures is easy to realize and measure. The structural dynamic detection method is not limited by the size and concealment of the structure, as long as the response sensor is installed in the accessible structural position [1] . At present, efficient modular and digital structural dynamic response measurement technology provides solid and effective technical support for structural dynamic testing methods. Therefore, how to use the vibration characteristics of the structure to detect global damage and structural damage has become an urgent problem to be solved.  \nGenerally, the damage identification method based on mathematical model is the optimization of structural parameters. There are various optimization algorithms [2, 3], but these algorithms consume time and cannot realize real-time identification of structural damage. In addition, for cases with many degrees of freedom, the results can be unstable. In recent years, artificial neural networks have been widely used in damage recognition. The relationship between structural damage and structural mechanical properties is established by using neural networks to identify structural damage. Local damage occurs continuously during the use of civil structures, and when the local damage accumulates to a certain extent, it will pose a threat to the safety and reliability of the structure. The problem of structural damage identification is highly nonlinear and complex system, so it is difficult to identify damage by traditional methods. Neural network has great advantages in knowledge acquisition, adaptive learning, error correction ability and so on. It has become a new method to study damage recognition. The key to solving problems of neural networks is the reasonable selection of network input parameters [4], and the modal property is a function of the physical parameters of the structure.  \nGenerally speaking, the health monitoring of complex structures is divided into four stages: abnormal detection of structures, location of damage types, assessment of damage degree and prediction of residual life of structures [5] . Structural damage monitoring and detection technology has been described in detail in many pieces of literature,  \nand can be divided into structural parameter identification technology, modal parameter identification technology and non-physical model technology from the perspective of structural parameter identification. The core technology of civil structure damage detection is pattern reco","cbCairePGlmwWxmj","https://ap.wps.com/l/cbCairePGlmwWxmj","pdf",1679304,1,9,"English","en",105,"# Introduction\n## Related work","[{\"question\":\"How does the method use modal parameters for damage identification?\",\"answer\":\"It analyzes structural natural frequency and constructs identification quantities for each process from modal parameters, then uses them as machine learning feature parameters to recognize damage.\"},{\"question\":\"What role does vegetation index time series play in the proposed monitoring model?\",\"answer\":\"After 5G filtering in the damaged area, it extracts the median curve of the vegetation index time series and compares it with real crop growth curves to construct the monitoring model.\"},{\"question\":\"Which surface damage types are used to verify the method’s performance?\",\"answer\":\"The approach is validated on concrete cracks, spalling, and exposed steel bars, showing good prediction and accurate pixel-level identification.\"}]","Research on Damage Detection of Civil Structures Based on Machine Learning of Multiple Vegetation Index Time Series | 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does the method use modal parameters for damage identification?","Question",{"text":75,"@type":76},"It analyzes structural natural frequency and constructs identification quantities for each process from modal parameters, then uses them as machine learning feature parameters to recognize damage.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does vegetation index time series play in the proposed monitoring model?",{"text":80,"@type":76},"After 5G filtering in the damaged area, it extracts the median curve of the vegetation index time series and compares it with real crop growth curves to construct the monitoring model.",{"name":82,"@type":73,"acceptedAnswer":83},"Which surface damage types are used to verify the method’s performance?",{"text":84,"@type":76},"The approach is validated on concrete cracks, spalling, and exposed steel bars, showing good prediction and accurate pixel-level 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