[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119726-en":3,"doc-seo-119726-105":30,"detail-sidebar-cat-0-en-105":84},{"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":20,"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},119726,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Damage assessment in beam-like structures by correlation of spectrum using machine learning","Damage assessment during real structural operation is a key and challenging issue in construction engineering because it requires reliable knowledge of the current state of inspected structures. A central difficulty lies in controlling excitation within the structure, which motivates output-based structural damage identification methods that avoid dependence on the excitation source. The study presents a supervised machine-learning approach using spectral correlation as input features for an ANN and a decision tree, predicting new cut appearance, cut level, and cut position, validated through a supported beam experiment with vibration data.","V. Le-Ngoc et alii, Frattura edIntegrità Strutturale, 65 (2023) 300-319; DOI: 10.3221/IGF-ESIS.65.20  \n| \u003Cbr>Damage assessment in beam-like structures by correlation of spectrum using machine learning\u003Cbr>Vien Le-Ngoc, Luan Vuong-Cong, Toan Pham-Bao*, Nhi Ngo-Kieu\u003Cbr>Laboratory ofApplied Mechanics (LAM), Faculty ofApplied Science, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, VietNam.\u003Cbr>[lnvien.sdh19@hcmut.edu.vn](lnvien.sdh19@hcmut.edu.vn), [http://orcid.org/0000-0002-8154-1014](http://orcid.org/0000-0002-8154-1014)[ ](http://orcid.org/0000-0002-8154-1014)[vuongluan@hcmut.edu.vn](vuongluan@hcmut.edu.vn), [http://orcid.org/0000-0003-4146-9297](http://orcid.org/0000-0003-4146-9297)[ ](http://orcid.org/0000-0003-4146-9297)[baotoanbk@hcmut.edu.vn](baotoanbk@hcmut.edu.vn), [https://orcid.org/0000-0002-2105-2403](https://orcid.org/0000-0002-2105-2403)[ ](https://orcid.org/0000-0002-2105-2403)[ngokieunhi@hcmut.edu.vn](ngokieunhi@hcmut.edu.vn), [https://orcid.org/0000-0001-9230-4308](https://orcid.org/0000-0001-9230-4308) |  |\n| --- | --- |\n| ABSTRACT. Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation.\u003Cbr>KEYWORDS. Damage identification, Artificial neural network (ANN), Decision Tree, Spectral correlation, Beam-like structure. | \u003Cbr>Citation: Le-Ngoc, V., Vuong-Cong, L., Pham-Bao, T., Ngo-Kieu N., Damage assessment in beam-like structures by correlation of spectrum using machine learning, Frattura ed Integrità Strutturale, xx (2023) 300-319.\u003Cbr>Received: 19.05.2023\u003Cbr>Accepted: 14.06.2023\u003Cbr>Online first: 20.06.2023\u003Cbr>Published: 01.07.2023\u003Cbr>Copyright: © 2023 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |\n| INTRODUCTION\u003Cbr>S tructural health monitoring (SHM) and damage detection play a vital role in ensuring the safety and entirety of the\u003Cbr>structures by assessing the damage development and predicting the remaining life cycle of the structural systems such as buildings, dams and bridges, etc. It is a process in the experimental data as vibration response can be used to detect |  |\n\nV. Le-Ngoc et alii, Frattura edIntegrità Strutturale, 65 (2023) 300-319; DOI: 10.3221/IGF-ESIS.65.20  \nand evaluate structural damage degrees appropriately. SHM process can be categorized into five levels [1]: (1) presenting damage, (2) localizing damage, (3) categorizing damage,(4) estimating damage severity, and (5) predicting the development of damage. As the main technique of SHM, structural damage detection has been intently applied for decades. Vibration signals are a popular big data source exploited to detect structural damage [2, 3] . These signals contain features that indicate sensitivity to struct","cbCaia8tOQ757ioH","https://ap.wps.com/l/cbCaia8tOQ757ioH","pdf",4591083,1,20,"English","en",105,"# Abstract\n## Structural health monitoring background\n## Limitations of frequency-domain features\n## Supervised machine learning approach\n## Experimental validation","[{\"question\":\"What damage parameters does the model output?\",\"answer\":\"The machine-learning outputs include the appearance of new cuts, the cutting level, and the cutting position.\"}]","Damage assessment in beam-like structures by correlation of spectrum using machine learning | PDF",1785725977,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":28},"damage-assessment-in-beam-like-structures-by-correlation-of-spectrum-using-machine-learning","",{"@graph":36,"@context":78},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/damage-assessment-in-beam-like-structures-by-correlation-of-spectrum-using-machine-learning/119726/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"What damage parameters does the model output?","Question",{"text":76,"@type":77},"The machine-learning outputs include the appearance of new cuts, the cutting level, and the cutting position.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":85},[86,90,94,98,103,107,112,115,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":99,"doc_module":4,"doc_module_name":46,"category_name":100,"show_sort_weight":101,"slug":102},5,"Comic",60,"comic",{"id":104,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":29,"slug":106},6,"Technology","technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":21,"slug":118},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":21,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":99,"slug":129},19,"General","general"]