Techno Press
Techno Press

Steel and Composite Structures   Volume 25, Number 4, November20 2017, pages 485-496
DOI: http://dx.doi.org/10.12989/scs.2017.25.4.485
 
Truss structure damage identification using residual force vector and genetic algorithm
Mehdi Nobahari, Mohammad Reza Ghasemi and Naser Shabakhty

 
Abstract     [Full Text]
    In this paper, damage detection has been introduced as an optimization problem and a two-step method has been proposed that can detect the location and severity of damage in truss structures precisely and reduce the volume of computations considerably. In the first step, using the residual force vector concept, the suspected damaged members are detected which will result in a reduction in the number of variables and hence a decrease in the search space dimensions. In the second step, the precise location and severity of damage in the members are identified using the genetic algorithm and the results of the first step. Considering the reduced search space, the algorithm can find the optimal points (i.e. the solution for the damage detection problem) with less computation cost. In this step, the Efficient Correlation Based Index (ECBI), that considers the structure\'s first few frequencies in both damaged and healthy states, is used as the objective function and some examples have been provided to check the efficiency of the proposed method; results have shown that the method is innovatively capable of detecting damage in truss structures.
 
Key Words
    damage detection; residual force vector; genetic algorithm; optimization; modal frequency; mode shape
 
Address
Mehdi Nobahari and Mohammad Reza Ghasemi: Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran
Naser Shabakhty: School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
 

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2019 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: technop@chol.com