مروری بر شناسایی آسیب و ترک در سازه ها با استفاده از الگوریتم ها و شبکه های عصبی مصنوعی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشیار، گروه مهندسی عمران، دانشکده فنی مهندسی، دانشگاه محقق اردبیلی ، اردبیل، ایران

2 دانشجوی دکتری، گروه مهندسی عمران، دانشکده فنی مهندسی، دانشگاه محقق اردبیلی، اردبیل، ا یران

چکیده

نظارت بر آسیب های سازه ای، برای حفظ عمر مفید سازه های عمرانی بسیار مهم است. روش‌ های نظارت بسیاری برای ارائه ابزار های عملی برای هشدار اولیه در برابر آسیب های سازه ای یا هر نوع نا هنجاری ایجاد شده‌ اند. لذا روش نظارت بر سلامت سازه، امروزه یک رویکرد اصلی برای مدیریت شناسایی و تشخیص آسیب در مناطق مختلف به شمار می آید. نیاز به نظارت بر رفتار سازه، هر روز در حال افزایش است، اما به دلیل توسعه مصالح جدید و سازه های پیچیده تر، این امر، منجر به توسعه روش‌ های نظارت بر سلامت سازه قوی و حساس می شود. هوش مصنوعی، یک رویکرد جایگزین کارآمد برای روش های مدل سازی کلاسیک است. راه حل‌ های مبتنی بر هوش مصنوعی، جایگزین‌ های خوبی برای تعیین پارامتر های طراحی مهندسی در مواقعی هستند که آزمایش امکان پذیر نیست؛ بنابراین منجر به صرفه‌ جویی قابل توجهی در زمان و تلاش انسان در آزمایش ها می شود. امروزه، یادگیری ماشینی به موفق ترین زیر شاخه هوش مصنوعی تبدیل شده است. شناسایی آسیب با استفاده از پردازش سیگنال هوشمند و الگوریتم های بهینه سازی مبتنی بر معیار های ارتعاش از جمله مواردی هست که حائز اهمیت می باشد. در این مقاله، برخی از مطالعات اخیر در زمینه کاربرد های شبکه های عصبی مصنوعی برای شناسایی آسیب و ترک بررسی شده است. سعی شده است تا مروری جامع بر مقالات مطرح شده در زمینه کاربرد روش های بهینه سازی و روش های معکوس، هوش مصنوعی و یادگیری ماشین، ارزیابی آسیب و ترک در سازه های مختلف با استفاده از شبکه های عصبی مصنوعی، با نگاهی ویژه بر مطالعات انجام شده در دهه های گذشته، انجام شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

An overview of damage and crack detection in structures using metaheuristic algorithms and artificial neural networks

نویسندگان [English]

  • Amin Ghannadiasl 1
  • Saeedeh Ghaemifard 2
1 Associate Professor, Civil Engineering Department, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
2 Ph.D. student, Civil Engineering Department, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
چکیده [English]

Monitoring structural health is very important to maintain the useful life of civil structures. Many monitoring methods have been developed to provide practical tools for early warning against structural damage or any type of anomaly. Therefore, the health monitoring method of the structure is considered the main approach for the management of identification and diagnosis of damage in different areas. The need to monitor the behavior of the structure is increasing every day, but due to the development of new materials and more complex structures, this leads to the development of strong and sensitive methods for the health monitoring of the structure. Artificial intelligence is an efficient alternative approach to classical modeling methods. Solutions based on artificial intelligence are good alternatives for determining engineering design parameters when testing is not possible; Therefore, it leads to a significant saving of human time and effort in experiments. Today, machine learning has become the most successful sub-branch of artificial intelligence. Identifying damage using intelligent signal processing and optimization algorithms based on vibration criteria is one of the important things. Some recent studies on the applications of artificial neural networks for damage and crack detection have been reviewed in this paper. An attempt has been made to provide a comprehensive review of the published articles in the field of application of optimization methods and inverse methods, artificial intelligence and machine learning, and assessment of damage and cracks in various structures using artificial neural networks, with a view, especially on the studies conducted in the past decades.

کلیدواژه‌ها [English]

  • Artificial neural networks
  • Optimization
  • Machine learning
  • Structural health monitoring
  • Crack and damage detection
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