نویسنده
دانشکده مهندسی عمران، دانشگاه سیستان و بلوچستان
چکیده
امروزه استفاده از مدل‌های جایگزین مبتنی بر الگوریتم‌های هوش مصنوعی در زمینه عیب‌یابی سازه‌ها مورد توجه بسیاری از محققین قرار گرفته است. در این تحقیق، جهت افزیش دقت الگوریتم تشخیص عیوب چندگانه در سیستم‌های سازه‌ای، کرنل جدیدی مبتنی بر موجک لیتلود پالی برای الگویتم هوش مصنوعی ماشین یادگیری حداکثر، توسعه داده می‌شود. به‌منظور کاهش حجم محاسبات به‌روزرسانی مدل سازه از ماشین یادگیری حداکثر به‌عنوان مدل جایگزین برای تحلیل اجزای محدود دقیق سازه استفاده می‌شود. در روش دو مرحله‌ای پیشنهادی در مرحله اول با استفاده از شاخص مبتنی بر تابع پاسخ فرکانسی سازه، المان‌های معیوب مشخص می‌شود و در مرحله دوم شدت خرابی در این اعضا با استفاده از مدل جایگزین مبتنی بر ماشین یادگیری حداکثر تعیین می‌شود. برای مقایسه کارایی سیستم مبتنی بر ماشین یادگیری حداکثر، نتایج حاصل از کرنل پیشنهادی با سایر کرنل‌های پیشنهاد شده برای ماشین یادگیری حداکثر و همچنین الگوریتم ماشین بردار پشتیبان حداقل مربعات، مقایسه شده است. مثال‌های عددی حل‌شده بیانگر افزایش قابل توجه دقت الگوریتم ماشین یادگیری حداکثر در فرایند عیب‌یابی سازه‌ها در صورت استفاده از کرنل موجکی لیتلود پالی است.
کلیدواژه‌ها
عنوان مقاله [English]
Structural Damage Detection using Frequency Response Function Index and Surrogate Model Based on Optimized Extreme Learning Machine Algorithm
نویسنده [English]
- R. Ghiasi
چکیده [English]
Utilizing surrogate models based on artificial intelligence methods for detecting structural damages has attracted the attention of many researchers in recent decades. In this study, a new kernel based on Littlewood-Paley Wavelet (LPW) is proposed for Extreme Learning Machine (ELM) algorithm to improve the accuracy of detecting multiple damages in structural systems. ELM is used as metamodel (surrogate model) of exact finite element analysis of structures in order to efficiently reduce the computational cost through updating process. In the proposed two-step method, first a damage index, based on Frequency Response Function (FRF) of the structure, is used to identify the location of damages. In the second step, the severity of damages in identified elements is detected using ELM. In order to evaluate the efficacy of ELM, the results obtained from the proposed kernel were compared with other kernels proposed for ELM as well as Least Square Support Vector Machine algorithm. The solved numerical problems indicated that ELM algorithm accuracy in detecting structural damages is increased drastically in case of using LPW kernel.
کلیدواژه‌ها [English]
- Detecting structural damages
- Surrogate model
- Extreme Learning Machine (ELM) algorithm
- Littlewood-Paley Wavelet (LPW) kernel
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