TY - JOUR
T1 - Structural Damage Detection Using Artificial Bee Colony Optimization Algorithm
TT - تشخیص آسیب سازه با استفاده از الگوریتم بهینهسازی کلونی زنبور عسل مصنوعی
JF - mdrsjrns
JO - mdrsjrns
VL - 19
IS - 3
UR - http://mcej.modares.ac.ir/article-16-16870-en.html
Y1 - 2019
SP - 17
EP - 29
KW - Damage detection
KW - Artificial bee colony algorithm
KW - SEREP codensatation.
N2 - In recent years, the damage identification of structures becomes more attractive for researchers in order to assess the quantify condition of structural system during service life. Moreover, identifying the damage location and severity is very important after disaster such as earthquake and terrorist attak. Structures can be also damaged by normal activity such as corrosion, aging, fatique, wind, waveload etc. Therefore the structural health monitoring is an emerging field to ensure the continues and periodic performance of structures. In this paper, identification of the extent and location of damages in structures are studied by analytical method using artificial bee colony optimization (ABC). In the analytical method, the mass and stiffness matrices of structure could be determine by the finite element procedure. Considering the stiffness matrix of healthy structure and that of the damage structure, the location and severity of the damage could be determined. It is assumed that the global mass matrix remains unchanged after the damage occures in the structure. The natural frequencies and mode shapes of damaged structure can be obtained by measurement. In the study, the damage characteristics are known. Then by applying the eigenvalue equation, the stiffness matrix is determined for damaged structure. Finding the extent and location of damage is introduced as an inverse problem. Using the conventional methods are very expensive and time consuming, while metaheuristic evolutionary computing method is capable to solve complex combinational optimization problems. Swarm intelligence algorithm introduces the collactive behavior of social insects colonies to solve optimization problems. Artificial bee colony algoritm is an evolutionary computing method, which is developed, based on the intelligent foraging behavior of honeybee swarm. Each food source is considered as a possible solution. The location and quality of the nectar from the flower is related to the damage properties and fitness function, respectively. The dimension of every artificial employed bee is equal to the number of member of the structure. Then quality value of the food source is evaluated by the fitness function. The best fitness value is memorized in each search. When the fitness value denote improved after a predefined iterative, the new possible solution will be considered. In the ABC process, the number of food source, the limit and the maximum cycle number are three control parameters. In the optimization problem, applying a proper objective function is one of the indispesable part of the process. Since the structural damage detection is a highly nonlinear problem, a proper objective function can detect the damage accurately and quackly. There are various methods for damage detection, which generaly can be classified into two categories, static and dynamic method. Because of the efficiency of the dynamic method, the objective function is selected based on the dynamic technique, which utilize the eigenvalue problem. In the mathematical equation of the objective function, the mass and stiffness matrix of healthy structure is defined by finite element method. The natural frequencies and mode shapes obtained by the measurement. The stiffness matrix of damaged structure is determined with the optimization algorithm to minimize the objective function. In a measurement test, the used sensors cannot detect all of the degrees freedom of a structure, therefore the obtained information in measurement include a limited number of frequencies or mode shapes. In addition, to avoid a time consuming process, it may be decided to utilize only a limit number of frequencies obtained by the measurement. The system equivalent reduction expansion process (SEREP), which is an accurate and efficient technique of model reduction, is utilized in the paper. Moreover, the damage detection is examined through three numerical examples, plane and space truss and palne frame, each one has two damage scenarios, which include noisy measurement data. The results indicate that the proposed method is a powerfull procedure to detect damages in structures.
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