Response surface methodology for damage detection using frequency and mode shape

Abstract Response surface methodology (RSM) has been proven applicable for updating finite element baseline models. Most RSM-based damage detection methods are frequency-based, limiting their application to small structures and symmetrical damage. This study therefore proposed a new RSM method that employs both natural frequencies and mode shapes. The efficiency of the proposed damage detection method is demonstrated through a numerical model of a simply supported beam and a laboratory-tested steel frame. To choose the best RSM design for damage detection, the effects of design of experiment (DOE) to the damage detectability is investigated. The DOEs studied include central composite design (CCDMRV and CCD64), Box-Behnken design (BBD) and D-optimal design (Dopt). The results show that RSM is a potentially useful approach for damage detection. The RS model based on the CCD sampling method with bigger sample size provides the best damage detection performance.

[1]  C. Fritzen,et al.  DAMAGE DETECTION BASED ON MODEL UPDATING METHODS , 1998 .

[2]  Yi-Qing Ni,et al.  Structural Damage Detection of Cable-Stayed Bridges Using Changes in Cable Forces and Model Updating , 2009 .

[3]  Zhihong Zhang,et al.  Comparison about the Three Central Composite Designs with Simulation , 2009, 2009 International Conference on Advanced Computer Control.

[4]  Wei-Xin Ren,et al.  Finite element model updating in structural dynamics by using the response surface method , 2010 .

[5]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[6]  Ricardo Perera,et al.  Damage identification by response surface based model updating using D-optimal design , 2011 .

[7]  Ricardo Perera,et al.  A response surface methodology based damage identification technique , 2009 .

[8]  Sara Casciati,et al.  Response Surface Models to Detect and Localize Distributed Cracks in a Complex Continuum , 2010 .

[9]  Robert D. Adams,et al.  The location of defects in structures from measurements of natural frequencies , 1979 .

[10]  Marco Savoia,et al.  Coupling Response Surface and Differential Evolution for Parameter Identification Problems , 2015, Comput. Aided Civ. Infrastructure Eng..

[11]  O. S. Salawu.  Detection of structural damage through changes in frequency: a review , 1997 .

[12]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[13]  Pizhong Qiao,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[14]  Wang Hu,et al.  Optimization of sheet metal forming processes by adaptive response surface based on intelligent sampling method , 2008 .

[15]  Rongming Lin,et al.  Structural damage detection using measured FRF data , 1997 .

[16]  Hojjat Adeli,et al.  A probabilistic neural network for earthquake magnitude prediction , 2009, Neural Networks.

[17]  Hoon Sohn,et al.  Damage diagnosis using time series analysis of vibration signals , 2001 .

[18]  Maria Pina Limongelli,et al.  Damage localization in bridges via the FRF interpolation method , 2015 .

[19]  Wei-Xin Ren,et al.  Response Surface―Based Finite-Element-Model Updating Using Structural Static Responses , 2011 .

[20]  Lu Deng,et al.  Bridge Model Updating Using Response Surface Method and Genetic Algorithm , 2010 .

[21]  Anupam Chakrabarti,et al.  Structural Damage Identification Using Response Surface-Based Multi-objective Optimization: A Comparative Study , 2015, Arabian Journal for Science and Engineering.

[22]  Jian Ping Han,et al.  Static and Dynamic Finite Element Model Updating of a Rigid Frame-Continuous Girders Bridge Based on Response Surface Method , 2013 .

[23]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[24]  Hong Hao,et al.  Structure Damage Detection Using Neural Network with Multi-Stage Substructuring , 2010 .

[25]  Hoon Sohn,et al.  A review of structural health monitoring literature 1996-2001 , 2002 .

[26]  Akira Mita,et al.  An improved substructural damage detection approach of shear structure based on ARMAX model residual , 2016 .

[27]  Wei-Xin Ren,et al.  Damage detection by finite element model updating using modal flexibility residual , 2006 .