چکیده مقاله
Structural Health Monitoring SHM is a critical aspect of ensuring the safety and longevity of civil infrastructure Traditional methods have limitations in providing real time insights into structural conditions, leading to a demand for more advanced solutions This article explores the application of machine learning in SHM for efficient and accurate damage detection The process encompasses data acquisition from various sensors, feature extraction, data preprocessing, and model selection Supervised and unsupervised learning algorithms, such as Support Vector Machines, Random Forests, and neural networks, are considered for training and validation The trained models are then deployed for real time damage detection, offering the potential for early intervention and proactive maintenance Continuous monitoring and adaptation ensure the system's resilience to changing conditions, while the integration of human expertise remains pivotal for interpreting results This holistic approach to SHM, empowered by machine learning, holds promise for revolutionizing how we monitor and manage the structural integrity of our built environment, paving the way for safer and more sustainable infrastructure
کلیدواژهها
نویسندگان
Seyed Reza Samaei
Post doctoral, Lecturerof Technical and Engineering Faculty,Science and Research Branch, Islamic Azad University,Tehran, Iran
Madjid Ghodsi Hassanabad
Associate Professor,Department of Marine industries,Science and Research Branch, Islamic Azad University,Tehran, Iran
شیوه ارجاع
Samaei, Seyed Reza and Ghodsi Hassanabad, Madjid,1402,Machine Learning Approach for Structural Health Monitoring and Damage Detection,8th International Conference on Civil Engineering, Architecture, Urban Planning with Sustainable Development Approach,Tehran
ارائهشده در
مجموعه مقالات هشتمین همایش بین المللی مهندسی عمران، معماری، شهرسازی با رویکرد توسعه پایدار15 اسفند 1402 · تهران