تشخیص خرابی در پل کابلی مبتنی بر ویژگی‌های مود تجربی مؤثر در تبدیل موجک تجربی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی عمران، گروه عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

2 استاد، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران

3 استادیار، گروه عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

4 استادیار، گروه عمران، واحد رودهن، دانشگاه آزاد اسلامی، رودهن، ایران

چکیده

شناسایی هوشمندانه خسارت در سازه­های زیربنایی از جمله پل­ها به منظور بهبود عملکرد پیش­بینی آسیب و کاهش هزینه نگهداری ضروری است. بنابراین، توسعه روش­های کارآمد برای تشخیص به موقع آسیب در سازه­ و تصمیم­گیری نسبت به تعمیر آن از اهمیت زیادی برخوردار است. در این مقاله، یک روش جدید مبتنی بر ویژگی­های باند فرکانسی مؤثر در تحلیل تبدیل موجک تجربی به منظور تشخیص آسیب در پل کابلی ارائه شد که شامل دو بخش است: (1) پردازش سیگنال و استخراج ویژگی، (2) تشخیص آسیب با استفاده از ترکیب ویژگی­های مؤثر. در بخش اول، داده­های پاسخ سازه با استفاده از تبدیل موجک تجربی به مودهای تجربی تجزیه گردید و مجموعه­ای از ویژگی­­ها به عنوان مشخصه حساس به خرابی از طیف فرکانس مودها استخراج شد. به منظور ارزیابی نتایج از طبقه­بند ماشین بردار پشتیبان استفاده گردید. در بخش دوم، برای کاهش خطای الگوریتم شناسایی آسیب، با استفاده از روش­های انتخاب ویژگی، یک زیرمجموعه بهینه حاوی اطلاعات مربوط به آسیب سازه­ای شامل ترکیبی از ویژگی­های مهم به عنوان شاخص خرابی تعیین گردید. برای اعتبارسنجی روش پیشنهادی از داده­های پاسخ پل کابلی یونگ استفاده شد. نتایج نشان داد که دومین و سومین مود تجربی حاصل از تحلیل موجک تجربی حاوی اطلاعات مرتبط با خرابی بوده و بکارگیری طیف فرکانسی متناظر آن در فرآیند استخراج ویژگی، عملکرد شناسایی را نسبت به روش­های متداول حدود 5 درصد بهبود می­دهد. همچنین استفاده از ترکیب ویژگی­های مؤثر بجای یک ویژگی تنها، دقت شناسایی را به­ حدود 94 درصد افزایش می­دهد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Damage Assessment of a Cable-Stayed Bridge Based on Effective Empirical Mode Features using Empirical Wavelet Transform

نویسندگان [English]

  • Hossein Babajanian Bisheh 1
  • Gholamreza Ghodrati Amiri 2
  • Masoud Nekooei 3
  • Ehsan Darvishan 4
1 PhD Student, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Professor, Center of Excellence for Fundamental Studies in Structural Engineering, School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran
3 Assistant Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 Assistant Professor, Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
چکیده [English]

Intelligent damage detection of civil infrastructures is vital to improve damage prediction performance and reduce maintenance costs. Therefore, the development of efficient techniques for detecting structural damages in an early stage is extremely important to support making decisions on structure repair. In this paper, a new damage detection method based on effective frequency band with empirical wavelet transform for a cable-stayed bridge was proposed which consists of two stages: (1) signal processing and feature extraction, (2) damage identification by combining effective features. In the first stage, structural response data was decomposed into empirical modes using empirical wavelet transform to obtain the component related to structural damage, and a set of features as damage-sensitive features were extracted from the frequency spectrum of modes. A support vector machine was applied to evaluate the results. In the second stage, by applying feature selection methods, an optimal subset of features that carries the most significant information about the structural damage was obtained as a damage index. Next, it was used in the feature extraction process. To verify the proposed damage detection method, response data obtained from a cable-stayed bridge, the Yonghe Bridge, was employed. Results showed that the second and third empirical modes obtained from the empirical wavelet analysis contain fault information and using its corresponding frequency spectrum in the feature extraction process improves detection performance by about 5% compared to conventional methods. It also increases the detection accuracy to about 94% by employing effective feature combinations rather than a single feature.

کلیدواژه‌ها [English]

  • damage detection
  • empirical wavelet transform
  • feature selection
  • Cable-Stayed Bridge
  • Feature Extraction
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