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Narges Dehghani Dehnavi, Narges, Bitaraf, Maryam. (1404). Development of a Digital Twin of a Laboratory Structure for Machine Vision-Based Structural Health Monitoring Approach. , (), -. doi: 10.22059/ceij.2025.385472.2183
Narges Narges Dehghani Dehnavi; Maryam Bitaraf. "Development of a Digital Twin of a Laboratory Structure for Machine Vision-Based Structural Health Monitoring Approach". , , , 1404, -. doi: 10.22059/ceij.2025.385472.2183
Narges Dehghani Dehnavi, Narges, Bitaraf, Maryam. (1404). 'Development of a Digital Twin of a Laboratory Structure for Machine Vision-Based Structural Health Monitoring Approach', , (), pp. -. doi: 10.22059/ceij.2025.385472.2183
Narges Dehghani Dehnavi, Narges, Bitaraf, Maryam. Development of a Digital Twin of a Laboratory Structure for Machine Vision-Based Structural Health Monitoring Approach. , 1404; (): -. doi: 10.22059/ceij.2025.385472.2183

Development of a Digital Twin of a Laboratory Structure for Machine Vision-Based Structural Health Monitoring Approach

Civil Engineering Infrastructures Journal
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 03 اردیبهشت 1404    اصل مقاله (1.15 M)
شناسه دیجیتال (DOI): 10.22059/ceij.2025.385472.2183
نویسندگان
Narges Narges Dehghani Dehnavi1؛ Maryam Bitaraf* 2
1Enghelab Ave., 16 Azar Street, University Of Tehran
2School of Civil Engineering, University of Tehran
چکیده
This study presents a cost-effective Structural Health Monitoring (SHM) approach that integrates machine vision, digital twin technology, and machine learning. Machine vision serves as a sensor to capture the response of a three-story laboratory structure under base excitation, using the optical flow method and the Lucas-Kanade algorithm to track displacements. These measurements are validated against radar and accelerometer sensors, demonstrating the effectiveness of radar sensors for vibration-based displacement monitoring in SHM. A digital twin is then developed by integrating vibration data with a physics-based model to simulate structural behavior, enabling the detection of damage type, location, and severity under various conditions. Different machine learning classifiers are trained on data from both simulated and physical models, with the Manhattan distance-based classifier achieving the highest accuracy of 92%. The results indicate that this digital twin system offers a reliable tool for real-time SHM and predictive maintenance, facilitating early damage detection and enhancing structural resilience.
کلیدواژه‌ها
Machine learning؛ Optical flow algorithm؛ Lucas-Kanade algorithm؛ Physics-based model؛ Manhattan distance
آمار
تعداد مشاهده مقاله: 64
تعداد دریافت فایل اصل مقاله: 66





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