تشخیص خسارت در پل‌های بزرگ دهانه با وجود چند خسارت همزمان
محورهای موضوعی : آنالیز سازه - زلزله
محمد وحیدی
1
,
آرمین عطیمی نژاد
2
*
,
مریم فیروزی
3
,
محمد هریسچیان
4
1 - گروه مهندسی عمران، واحد تهران جنوب، دانشگاه آزاد اسالمی، تهران، ایران
2 - دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران، ایران
3 - هیات علمی دانشگاه آزاد اسلامی واحد تهران جنوب
4 - department of civil eng, islamic Azad Uni (south Tehran branch)
کلید واژه: تشخیص خسارت‌چندگانه , خسارات ریز و کوچک, المان محدود طیفی, شاخص خسارت انرژی‌کرنشی مودال, رگرسیون بردار پشتیبان, الگوریتم ژنتیک,
چکیده مقاله :
مقاله حاضر یک روش دومرحله‌ای قدرتمند برای تشخیص خسارت پل‌های بزرگ دهانه با مقاطع متغیر ارائه می‌نماید. پل‌ها یکی از زیرساخت‌های اساسی در حوزه حمل‌ونقل شهری و برون شهری بوده که تشخیص خسارت به موقع درطول بهره‌برداری آن حائز اهمیت می‌باشد. خسارت دراین دسته از سازه‌ها سبب اختلال درخدمت‌رسانی درزمان بروزبلایای طبیعی‌خواهد شد. روش ارائه شده بر مبنای ترکیب المان محدود طیفی و شاخص خسارت انرژی کرنشی مودال و همچنین ترکیب الگوریتم ژنتیک و رگرسیون بردار پشتیبان برای تشخیص وتخمین میزان شدت‌خسارت می‌باشد. یکی از روش‌های کارآمد درحوزه انتشار امواج روش المان محدود طیفی بوده که از قابلیت مدلسازی با انعطاف‌پذیری بالا و تشخیص خسارات ریز می‌باشد. روش‌های مبتنی بر ارتعاش بطور گسترده برای تشخیص خسارت سازه‌ها استفاده می‌گردد درحالیکه شاخص خسارت انرژی کرنشی مودال از حساسیت بالاتری در تشخیص خسارت در میان دیگر روش‌های مبتنی برارتعاش برخوردار است. مدل مورد تحقیق، پل‌کروچایلد درغرب کانادا می‌باشد که دارای ویژگی‌های خاصی از نظرهندسی و هم از مشخصات المان‌های سازهای می‌باشد. در این تحقیق شاخص خسارت انرژی کرنشی مودال به علت تغییر مقطع در طول شاه‌تیر‌ها اصلاح گردیده است. همچنین از رگرسیون بردار پشتیبان به عنوان یک تکنیک قدرتمند در تخمین میزان شدت خسارت استفاده شده است. جهت افزایش دقت و بهبود روش تخمین میزان شدت خسارات از الگوریتم ژنتیک برای بهینه‌سازی پارامترهای مؤثر رگرسیون بردار پشتیبان استفاده می‌گردد. روش ترکیبی الگوریتم ژنتیک و رگرسیون بردار پشتیبان توانسته است به نحو مطلوبی شدت خسارات را تخمین بزند.
This paper presents a powerful two-step method for damage detection of large-span bridges with variable sections. Bridges are one of the basic infrastructures in the field of urban and suburban transportation, and timely detection of damage during its operation is important. Damage in this category of structures will cause service disruption during natural disasters. The presented method is based on the combination of spectral finite element and modal strain energy damage index, as well as the combination of genetic algorithm and support vector regression to detect and estimate the damage severity. One of the efficient methods in the field of wave propagation is the spectral finite element method, which is capable of modeling with high flexibility and detecting micro damage. Vibration-based methods are widely used to detect structural damage, while the modal strain energy damage index has a higher sensitivity in detecting damage among other vibration-based methods. The case study model is the Crowchild Bridge in Western Canada, which has special characteristics in terms of geometry and the characteristics of structural elements. In this research, the modal strain energy damage index has been modified due to the change of cross-section along the girders. Also, support vector regression has been used as a robust technique in estimation damage severity. In order to increase the accuracy and improve the damage severity estimation method, the genetic algorithm is used to optimize the effective parameters of the support vector regression. The combined method of genetic algorithm and support vector regression has been able to estimate the severity of damages in a favorable way.
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