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Öğe Comparison of ABC, CPSO, DE and GA Algorithms in FRF Based Structural Damage Identification(Carl Hanser Verlag, 2013) Gökdağ, HakanIn this contribution, performances of well-known population based algorithms, the artificial bee colony (ABC), contemporary particle swarm optimization (CPSO), genetic algorithm (GA), and differential evolution (DE) are compared in a basic model for damage identification (DI). DI is modeled as an inverse problem with the objective function based on the difference of the frequency response functions (FRF) computed by the finite element model of the structure and the reference data measured from damaged structure. Damage parameters are determined solving the problem with the aforementioned algorithms. It was observed that DE is the best one of a given number of function evaluations and gives the most accurate results in spite of noise interference to the reference data. According to the relevant literature, this is the first study including a comparison of these algorithms in an FRF based DI study.Öğe A Crack Identification Approach for Beam-Like Structures under Moving Vehicle using Particle Swarm Optimization(Carl Hanser Verlag, 2013) Gökdağ, HakanA crack identification method for beam type structures under moving vehicle is proposed. The basic of the method is to formulate damage detection as an inverse problem, and solve for damage locations and extents. With respect to this, an objective function is defined based on the difference of damaged beam dynamic response and the response calculated by the mathematical model of the beam. The optimization problem is solved by the particle swarm optimization (PSO) with linearly increasing inertia weight to obtain crack locations and their depths. By the numerical simulations, it was observed that cracks with depth ratio of 0.1 can be identified with reasonable error by the present method in spite of noise interference of 3 %.Öğe A crack identification method for beam type structures subject to moving vehicle using particle swarm optimization(2013) Gökdağ, HakanIn this work a crack identification method for beam type structures under moving vehicle is proposed. The basic of the method is to formulate damage detection as an inverse problem, and solve for damage locations and extents. To this end, an objective function is defined based on the difference of damaged beam dynamic response and the response calculated by the mathematical model of the beam. The optimization problem is solved through a popular evolutionary algorithm, i.e. the particle swarm optimization (PSO) with constriction factor, to obtain crack locations and depths. From the numerical simulations it was observed that cracks with depth ratio of 0.1 can be identified with reasonable error by the present method in spite of noise interference and distortive effect of road surface roughness.Öğe A crack identification method for bridge type structures under vehicular load using wavelet transform and particle swarm optimization(2013) Gökdağ, HakanIn this work a crack identification method is proposed for bridge type structures carrying moving vehicle. The bridge is modeled as an Euler-Bernoulli beam, and open cracks exist on several points of the beam. Half-car model is adopted for the vehicle. Coupled equations of the beam-vehicle system are solved using Newmark-Beta method, and the dynamic responses of the beam are obtained. Using these and the reference displacements, an objective function is derived. Crack locations and depths are determined by solving the optimization problem. To this end, a robust evolutionary algorithm, that is, the particle swarm optimization (PSO), is employed. To enhance the performance of the method, the measured displacements are denoised using multiresolution property of the discrete wavelet transform (DWT). It is observed that by the proposed method it is possible to determine small cracks with depth ratio 0.1 in spite of 5% noise interference. © 2013 Hakan Gökda?.Öğe Multi-step diferansiyel transform metodu ile uç kütle eklentili kirişlerin serbest titreşim analizi(2019) Doğanay Katı, Hilal; Gökdağ, HakanKiriş-uç kütle sistemlerinin dinamik analizi robot kolları ve manipulatörler gibi mekanik sistemlerin başarılı bir şekilde tasarlanması açısından oldukça önemlidir. Literatürdeki birçok çalışmada bu sistemlerin serbest titreşimini analitik olarak çözümlemek için az sayıda değişken kesitli kiriş modeli dikkate alınmış, çoğunlukla sabit kesitli kiriş modeli kullanılmıştır. Ayrıca, uç kütlenin noktasal olduğu, kiriş ve uç kütle koordinat merkezlerinin çakışık olduğu kabul edilmiştir. Mevcut çalışmada burulmaya ve iki farklı düzlemde eğilmeye maruz, kiriş ve uç kütle merkezlerinin çakışık olmadığı bir sistem ele alınmış ve serbest titreşim analizi için yarı-nümerik yöntem olan Multi-Step Diferansiyel Transform Metodu (MDTM) uygulanmıştır. Sistemin doğal frekansları ve mod şekilleri iki farklı sınır şartı (sol ucun ankastre veya serbest olma durumu) için elde edilmiştir. Ayrıca, kiriş uzunluğu, uç kütle boyutları, kesit daralma oranı (taper ratio) gibi parametrelerin doğal frekanslar üzerindeki etkisi incelenmiştir. Elde edilen sonuçların doğruluğu yaygın bir şekilde kullanılan sonlu eleman yazılımı (ANSYS) ile karşılaştırılmış ve yeterince uyumlu olduğu gözlenmiştir.Öğe Structural Damage Detection Using Modal Parameters and Particle Swarm Optimization(Carl Hanser Verlag, 2012) Gökdağ, Hakan; Yıldız, Ali RızaVibration based on structural damage detection (DD) is an important subject in many fields of engineering. Detection of possible damage locations before destructive stiffness losses in the engineering structures occur, is a main goal of DD. This paper describes the damage detection in structural elements by means of Particle Swarm Optimization algorithm (PSO). In this regard, the finite element model of a Timoshenko beam is considered, and damage is assumed as a stiffness loss in some elements. Damage locations and extents are identified minimizing some well-known modal parameter based objective functions. It is concluded that modal flexibility is the best among the considered damage indexes. Also, the results show that PSO is an effective optimization approach in structural damage detection.
