پایش سلامت سازه ای پل های فولادی بر اساس اطلاعات مودال شرایط بهره برداری به همراه ارزیابی عدم قطعیت

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی عمران، دانشکده عمران، دانشگاه تبریز، تبریز، ایران

2 دانشیار، دانشکده عمران، دانشگاه تبریز، تبریز، ایران

3 استادیار، دانشگاه تبریز، تبریز، ایران

چکیده

تشخیص عیوب سازه­ای بر این اساس است که پاسخ دینامیکی سازه در اثر خرابی تغییر خواهد کرد. به این ترتیب امکان تعیین محل و شدت آسیب به کمک بررسی تغییر پاسخ سازه، قبل و بعد از ایجاد خرابی فراهم می­شود. در این تحقیق سیستم ژنتیک فازی برای پایش سلامت سازه پل بکار گرفته شده است. هدف کلیدی از بکارگیری الگوریتم ژنتیک، طراحی سیستم فازی خودکار می­باشد. از این روش برای تشخیص خرابی یک پل تک دهانه راه­آهن با تیرهای فولادی و یک پل بتنی استفاده شده است. برای مطالعه تشخیص خرابی، مدل­های عددی این دو پل به وسیله مشخصات دینامیکی اندازه­گیری شده آن­ها ساخته شده است. برای ارزیابی کارایی سیستم ژنتیک فازی در تشخیص خرابی و تأثیر شیوه مدل­سازی، از دو مدل اجزای محدود سه بعدی و مدل دو بعدی ساده شده تیر استفاده شده است. بعد از انجام تحلیل برای کنترل عدم قطعیت­ها، به فرکانس­های اندازه­گیری شده مقادیری نویز اضافه شده و اثر آن در موفقیت روش شناسایی بررسی شده است. بررسی­های این تحقیق نشان می­دهد که فرکانس طبیعی دارای حساسیت مناسب نسبت به اعمال سناریوهای خرابی مختلف در سازه می­باشد. علاوه بر این فرکانس طبیعی در مقایسه با سایر پارامترهای مودال، حساسیت کمتری نسبت به خطای نامطمئنی دارد. افزایش تعداد مودهای اندازه­گیری شده و بکارگیری مودهای پیچشی، موجب تشخیص دقیق خرابی حتی در سازه­های متقارن خواهد شد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Steel bridges structural health monitoring based on operational modal analysis accommodating evaluation of uncertainty

نویسندگان [English]

  • Saeid Jahan 1
  • Yusef Hoseinzadeh 2
  • Alireza Mojtahedi 3
1 MSc student, Department of Civil Engineering, University of Tabriz, Tabriz, Iran
2 Associate Professor, Department of Civil Engineering, University of Tabriz, Tabriz, Iran
3 Assistant Professor, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
چکیده [English]

Structural damage detection is based on that the dynamic response of structure will change because of damage. Hence, it is possible to estimate the location and severity of damage leads to changes in the dynamic response before and after the damage. In this study, the genetic fuzzy system has been used for bridge structural health monitoring. A key objective of using genetic algorithms is to automate the design of fuzzy systems. This method is used for damage detection of a single span railway bridge with steel girders and a concrete bridge. For studying damage detection, the numerical models of these two bridges are built with the measured dynamic characteristics. A three-dimensional finite element model and a single two-dimensional girders model of the bridge have been constructed to study usefulness of the genetic fuzzy system for damage detection and the effectiveness of modeling. After analysis to control the uncertainties, the measured frequencies are contaminated with some noise and the effect of that on the achievement of damage detection method is evaluated. The present study has shown that the natural frequency has appropriate sensitivity to different damage scenarios in the structure. In addition, the natural frequency in comparison with other modal parameters, is less affected by random noise. Increasing the number of measurement modes and using torsional modes, will lead to an accurate damage diagnosis even in symmetrical structures.

کلیدواژه‌ها [English]

  • Damage detection
  • Genetic Fuzzy system
  • Bridge structural health monitoring
  • Uncertainty
  • Frequency
[1]  Kullaa, J. (2003). Damage Detection of the Z24 Bridge Using Control Charts. Mechanical Systems and Signal Processing 17, no. 1, 163-170.
[2]  Magalhães, F. and Cunha, Á. and Caetano, E. (2008). Dynamic monitoring of a long span arch bridge. Engineering Structures 30, no. 11, 3034-3044.
[3]  Ramos, L.F. and Marques, L. and Lourenço, P.B. and De Roeck, G. and Campos-Costa, A. and Roque, J. (2010). Monitoring historical masonry structures with operational modal analysis: Two case studies. Mechanical Systems and Signal Processing 24, no. 5, 1291-1305.
[4]  Rainieri, C. and Fabbrocino, G. and Manfredi, G. and Dolce, M. (2012). Robust output-only modal identification and monitoring of buildings in the presence of dynamic interactions for rapid post-earthquake emergency management. Engineering Structures 34, 436-446.
[5]  Magalhães, F. and Cunha, A. and Caetano, E. (2012). Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection. Mechanical Systems and Signal Processing 28, 212-228.
[6]  Orcési, A.D. and Frangopol, D.M. (2011). Optimization of assessment strategies for aging bridges. Applications of Statistics and Probability in Civil Engineering, CRC Press, 581-586.
[7]  Modares, M. and Waksmanski, N. (2013). Overview of Structural Health Monitoring for Steel Bridges. Practice Periodical on Structural Design and Construction 18, no. 3, 187-191.
[8]  ]Boller, C. (2000). Next generation structural health monitoring and its integration into aircraft design. International Journal of Systems Science 31, no. 11, 1333-1349.
[9]  Worden, K. and Staszewski, W.J. and Hensman, J.J. (2011). Natural computing for mechanical systems research: A tutorial overview. Mechanical Systems and Signal Processing 25, no. 1, 4-111.
[10]   Zou, Y. and Tong, L. and Steven, G. (2000). Vibration-based model-dependent damage (delamination) identification and health monitoring for composite structures—a review. Journal of Sound and vibration 230, no. 2, 357-378.
[11]   Doebling, S.W. and Farrar, C.R. and Prime, M.B. (1998). A summary review of vibration-based damage identification methods. Shock and vibration digest 30, no. 2, 91-105.
[12]   Salawu, O. (1997). Detection of structural damage through changes in frequency: a review. Engineering structures 19, no. 9, 718-723.
[13]   Pawar, P.M. and Ganguli, R. (2003). Genetic fuzzy system for damage detection in beams and helicopter rotor blades. Computer methods in applied mechanics and engineering 192, no. 16, 2031-2057.
[14]   Cattarius, J. and Inman, D. (2000). Experimental verification of intelligent fault detection in rotor blades. International Journal of Systems Science 31, no. 11, 1375-1379.
[15]   Suresh, S. and Omkar, S. and Ganguli, R. and Mani, V. (2004). Identification of crack location and depth in a cantilever beam using a modular neural network approach. Smart Materials and Structures 13, no. 4, 907.
[16]   Ross, T. and Sorensen, H. and Savage, S. and Carson, J. (1990). DAPS: expert system for structural damage assessment. Journal of Computing in Civil Engineering 4, no. 4, 327-348.
[17]   Yao, J.T. (1985). Safety and reliability of existing structures, Pitman Advanced Publishing Program.
[18]   Pawar, P.M. and Ganguli, R. (2011). Structural health monitoring using genetic fuzzy systems, Springer Science & Business Media.
[19]   Gillet, G. (2010). Simply supported composite railway bridge: a comparison of ballasted and ballastless alternatives. Case of the Banafjäl Bridge,  KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Design and Bridges. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.(Järnvägsgruppen-Infrastruktur), KTH - Royal Institute of Technology.
[20]   Karoumi, R. and Wiberg, J. (2006). Kontroll av dynamiska effekter av passerande tåg på Botniabanans broar: sammanfattning, KTH - Royal Institute of Technology, Structural Design and Bridges, Stockholm.
[21]   Shu, J. and Zhang, Z. and Gonzalez, I. and Karoumi, R. (2013). The application of a damage detection method using Artificial Neural Network and train-induced vibrations on a simplified railway bridge model. Engineering Structures 52, 408-421.
[22]   Pawar, P.M. and Ganguli, R. (2007). Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades. Mechanical Systems and Signal Processing 21, no. 5, 2212-2236.
[23]   Ganguli, R. (2001). A fuzzy logic system for ground based structural health monitoring of a helicopter rotor using modal data. Journal of Intelligent Material Systems and Structures 12, no. 6, 397-407.
[24]   Beena, P. and Ganguli, R. (2011). Structural damage detection using fuzzy cognitive maps and Hebbian learning. Applied Soft Computing 11, no. 1, 1014-1020.
[25]   Chandrashekhar, M. and Ganguli, R. (2009). Uncertainty handling in structural damage detection using fuzzy logic and probabilistic simulation. Mechanical Systems and Signal Processing 23, no. 2, 384-404.
[26]   Liu, H. and Jiao, Y. and Cheng, Y. and Gong, Y. (2012). Reduction of uncertainties for damage identification of bridge based on fuzzy nearness and modal data. Journal of Applied Mathematics.
[27]   Marwala. T. (2010). Finite element model updating using computational intelligence techniques: applications to structural dynamics, Springer Science & Business Media.
[28]   Abe, S. (2012). Pattern classification: neuro-fuzzy methods and their comparison, Springer Science & Business Media.
[29]   Beygi, H. (2015). Vibration Control of a High-Speed Railway Bridge Using Multiple Tuned Mass Dampers,  KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges, KTH - Royal Institute of Technology.