نویسندگان
دانشکده مهندسی عمران، دانشگاه سیستان و بلوچستان، زاهدان
چکیده
پژوهش حاضر به مبحث کلان داده‌ها در حوزه پایش سلامت سازه‌ها می‌پردازد. بدین منظور، بعد از استخراج ویژگیهای پاسخ شتاب سازه، با استفاده از الگوریتم بهینه‌ساز ویژگیهای کم‌اثر و اضافی حذف می‌شوند و با انتخاب ویژگیهای تاثیرگذار و کاهش ابعاد داده‌ها دقت و سرعت روند عیب‌یابی سازه‌ها افزایش می‌یابد. انتخاب زیرمجموعه‌ای از ویژگی‌ها با استفاده از الگوریتم فراکاوشی هارمونی-پرندگان پیشنهادی در این پژوهش صورت خواهد پذیرفت که موجب افزایش تطبیق‌پذیری روند پیشنهادی در مواجه با کلان داده‌های ناشی از حسگرها و عدم قطعیت‌های ناشی از اختلال در داده‌های ورودی می‌شود. در روش پیشنهادی، برای استخراج ویژگیهای پاسخ شتاب از شاخص‌های مبتنی بر خصوصیات آماری و انرژی بسته‌های موجکی استفاده شده است. به‌علاوه از دو الگوریتم ماشین‌بردار پشتیبان حداقل مربعات موجکی وزن‌دار و شبکه عصبی تابع پایه شعاعی به‌عنوان مدل جایگزین تحلیل اجزای محدود سازه استفاده شده و با استفاده از آنها شدت و مکان خرابی در سازه‌ها شناسایی می‌شود. به‌عنوان مسائل کاربردی، عیب‌یابی سازه بنچ مارک گروه پایش سلامت سازه IASC-ASCE و سازه فضاکار ۱۲۰ عضوی مدنظر قرار گرفته است. نتایج نشان می‌دهد که انرژی بسته‌های موجکی حساسیت بالاتری نسبت به وجود خرابی در سازه نسبت به خصوصیات آماری دارد. به‌علاوه مقایسه الگوریتم ترکیبی هارمونی-پرندگان ارائه شده با چهار الگوریتم مطرح در حوزه عیب‌یابی، نشان‌دهنده سرعت و بازدهی بهتر این الگوریتم است. درنهایت استفاده از روش پیشنهادی موجب کاهش ۹۰ درصدی ابعاد داده‌ها در روند پایش سلامت سازه‌ها می‌شود
کلیدواژه‌ها
عنوان مقاله [English]
Feature Selection in Structural Health Monitoring Big Data Using a Meta-Heuristic Optimization Algorithm
نویسندگان [English]
- R. Ghiasi
- M. R. Ghasemi
چکیده [English]
This paper focuses on the processing of structural health monitoring (SHM) big data. Extracted features of a structure are reduced using an optimization algorithm to find a minimal subset of salient features by removing noisy, irrelevant and redundant data. The PSO-Harmony algorithm is introduced for feature selection to enhance the capability of the proposed method for processing the measured big data, which have been collected from sensors of the structure and uncertainties associated with this process. Structural response signals under ambient vibration are preprocessed according to wavelet packet decomposition (WPD) and statistical characteristics for feature extraction. It optimizes feature vectors to be used as inputs to surrogate models based on the wavelet weighted support vector machine (WWLS-SVM) and radial basis function neural network (RBFNN). Two illustrative test examples are considered, the benchmark dataset from IASC-ASCE SHM group and a 120-bar dome truss. The results indicate that the features acquired by WPT from vibrational signal have higher sensitivity to the damage of the structure. Furthermore, the proposed PSO-Harmony is compared with four well-known metaheuristic optimization algorithms. The obtaind results show that the proposed method has a better performance and convergence rate. Finally, the proposed feature subset selection method has the capability of 90% data reduction
کلیدواژه‌ها [English]
- Structural health monitoring
- Big data
- Surrogate model
- PSO-harmony algorithm
- Wavelet packet decomposition
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