Damage Diagnosis of Structures Using Modal Data and Static Response

Kourehli, Seyed Sina

doi:10.3311/ppci.7646

Cited by 7 publications

(7 citation statements)

References 23 publications

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“…Damage identification using three different objective functions based on residuals obtained from static and modal measurements has been proposed by Kourehli [21]. He applied simulated annealing to verify his method on a four-story steel frame proposed as IASC-ASCE structural health monitoring benchmark.…”

Section: Introductionmentioning

confidence: 99%

Modal Sensitivity Based Sensor Placement for Damage Identification Under Sparsity Constraint

Błachowski

2019

Period. Polytech. Civil Eng.

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The present study deals with a comprehensive approach for damage identification of spatial truss structures. The novelty of the proposed approach consists of a three-level analysis. First, sensitivity of assumed modal characteristics is calculated. Second, natural frequency sensitivity is used to determine hardly identifiable structural parameters and mode shape sensitivity is applied to select damage-sensitive locations of sensors. Third, two sparsity constrained optimization algorithms are tested towards efficient identification of applied damage scenarios. These two algorithms are based on ℓ1-norm minimization and non-negative least square (NNLS) solution.Performances of both proposed algorithms have been compared in two realistic case studies: the first one concerned a three-dimensional truss girder with 61 structural parameters and the second one was devoted to an upper-deck arch bridge composed of 416 steel members.

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Section: Introductionmentioning

confidence: 99%

Modal Sensitivity Based Sensor Placement for Damage Identification Under Sparsity Constraint

Błachowski

2019

Period. Polytech. Civil Eng.

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“…The structure is shown in Fig. 16(a), 59,60 which is a 4-story, 2-by-2 bay steel frame scale-model structure located in the Earthquake Engineering Research Laboratory at the University of British Columbia (UBC). Its dimensions are 2.5 m Â 2.5 m Â 3.6 m (width Â length Â height) and its physical characteristics are listed in Table 1.…”

Section: Properties Of the Benchmark Modelmentioning

confidence: 99%

Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Huang

Cheng

Zhang

et al. 2019

Int. J. Str. Stab. Dyn.

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Using the variations in parameters to detect structural damages has been widely used in damage identi¯cation of structures. When exposed to varying temperatures, not only the displacements and stresses of a structure will change, but also the elastic modulus of the materials, such as concrete and steel, of which the structure is made. Since the variation in elastic modulus will result in the variation of the sti®ness of the structure, a damage identi¯cation method without considering the temperature e®ects is, in principle, unacceptable. In this study, a damage identi¯cation method using the particle swarm optimization combined with the cuckoo search (PSO-CS) under the noise and temperature environment is proposed. First, the temperature variations are combined with the elastic modulus variation for addressing the temperature e®ects in¯nite element model. Second, a PSO-CS hybrid algorithm is adopted, which applies the updated mechanism of PSO in CS. Third, objective functions comprised of di®erent modal messages with diverse weight coe±cients are constructed for the damage identi¯cation and validated by numerical analysis of a simply supported beam. The results show that the performance of the PSO-CS is better than either PSO or CS individually. Finally, the PSO-CS is applied to the damage identi¯cation of ASCE Benchmark frame, for which the results indicate a satisfactory accuracy of the e®ectiveness of the proposed scheme.

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“…[5][6][7][8][9][10][11][12]. The effectiveness of the two mentioned techniques in diagnosing damages has been studied by researchers [13]. Among these studies, many researchers have used two dynamic characteristics simultaneously for improving the efficiency of their crack detection procedure [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Multiple Crack Detection in Thick-walled Pipes Using Artificial Bee Colony Algorithm

Banimahd¹,

Rahemi

2021

Period. Polytech. Civil Eng.

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An analytical method for diagnosis of cracks in thick-walled pipes with a circular hollow section is investigated in this study. In the proposed method, the defect is assumed to be a non-leaking crack, which is modeled by a massless linear spring with infinitesimal length at the crack location. In order to find the cracks in the pipe, the vibration-based method related to the modal properties of the pipe is utilized. In the modal analysis, the mass and stiffness matrices influence the dynamic properties of the pipe. It is assumed that the mass matrix remains unchanged after the crack initiation, while the corresponding stiffness matrix changes. The stiffness matrix of a cracked element can be formulated by the finite element method with two unknown parameters: location and depth of the crack. Using the eigensolution for an undamped dynamic system to formulate the objective function yields to a complicated optimization problem, which can be solved by an iterative numerical optimization method. Among the optimization approaches, the Artificial Bee Colony (ABC) algorithm is a simple and flexible technique for minimizing the objective function. In this paper, the analytical model is utilized to find the size and position of cracks in a pipe using the ABC algorithm and subsequently some numerical examples are examined in order to assess the accuracy of the method. The results show that the proposed method is able to acceptably estimate the location and depth of multiple cracks in the straight pipes as well as curved ones.

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Damage Diagnosis of Structures Using Modal Data and Static Response

Abstract: Abstract

Cited by 7 publications

References 23 publications

Modal Sensitivity Based Sensor Placement for Damage Identification Under Sparsity Constraint

Modal Sensitivity Based Sensor Placement for Damage Identification Under Sparsity Constraint

Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Multiple Crack Detection in Thick-walled Pipes Using Artificial Bee Colony Algorithm

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