{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/real-time-video-surveillance-based-structural","title":"Real-Time Video Surveillance Based Structural Health Monitoring of Civil Structures Using Artificial Neural Network","arxiv_id":null,"date":"2019-07-01","proceeding":"Journal of Nondestructive Evaluation 2019 7","authors":["Moushumi Medhi","Aradhana Dandautiya","Jagdish Lal Raheja"],"abstract":"Modern world’s incessantly increasing outdoor traffic load has eventually led to structural health concern and continuous\r\nhealth monitoring of large scale civil structures such as bridges, roads, highways, etc. In this paper, we propose a computer\r\nvision based non-destructive structural health monitoring (SHM) method using high speed camera system combined with\r\nthe brilliance of artificial intelligence. A number of appreciable SHM techniques had been reported that utilizes wired or\r\nwireless smart sensors, but the use of nondestructive techniques, such as, digital high speed imaging were rarely employed\r\nfor detection of dynamic vibrations of civil structures. In the current research, we have developed a high speed video imaging\r\nbased structural health monitoring system that utilizes blob detection based motion tracking algorithm. It provides factual\r\ninformation regarding localization and displacement of the target object or an existing feature in the civil structure. The\r\nmodal parameters were subsequently extracted to analyze the level of severity of structural damage within the civil structures.\r\nAlso, an artificial neural network is trained to infer the qualitative characteristics of structural vibrations based on vibration\r\nintensity and the network inferences can be correlated with the conditions of the structure. The efficacy of our vision system\r\nin remote measurement of dynamic displacements was demonstrated through a shaking table and a slip desk experiment. The\r\nexperimental results demonstrate real-time output with satisfactory performance.","url_abs":"https://link.springer.com/article/10.1007/s10921-019-0601-x","url_pdf":"https://link.springer.com/content/pdf/10.1007/s10921-019-0601-x.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"real-time-video-surveillance-based-structural","repo_url":"https://github.com/Moushumi9medhi/STRUCTURAL-HEALTH-MONITORING-SHM-","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"structural-health-monitoring","task_name":"Structural Health Monitoring"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}