• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3409-3416
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• 2012

Aim: It is well known that polycyclic aromatic hydrocarbons (PAHs) such as benzo (a) pyrene have carcinogenic properties and may cause many types of cancers in human populations. Genetic susceptibility might be due to variation in genes encoding for carcinogen metabolizing enzymes, such as cytochrome P-450 (CYP450). Our study aimed to investigate the effect of genetic polymorphisms of CYP1A1 (m1 and m2) on genetic damage in 115 coal-tar workers exposed to PAHs at their work place. Methods: Genetic polymorphisms of CYP1A1 were determined by the PCR-RFLP method. Comet and buccal micronucleus assays were used to evaluate genetic damage among 115 coal tar workers and 105 control subjects. Results: Both CYP1A1 m1 and CYP1A1 m2 heterozygous and homozygous (wt/mt+mt/mt) variants individually as well as synergistically showed significant association (P<0.05) with genetic damage as measured by tail moment (TM) and buccal micronuclei (BMN) frequencies in control and exposed subjects. Conclusion: In our study we found significant association of CYP1A1 m1 and m2 heterozygous (wt/mt)+homozygous (mt/mt) variants with genetic damage suggesting that these polymorphisms may modulate the effects of PAH exposure in occupational settings.

• Journal of Korean Society of Steel Construction
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• v.24 no.5
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• pp.549-558
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• 2012

This study identifies the damage detection of truss structures by using genetic algorithm(GA) from changed elements properties. To model the damaged truss structures, the modulus of elasticity of some specific elements is reduced. The analysis of truss structures is performed with static analysis by applying uniform load, and the location and extent of structural damage is detected by comparing the stain of each element of healthy truss structures with damaged truss structures using genetic algorithm. In this study, some numerical examples are presented to detect the location and extent of damage using genetic algorithm.

• Smart Structures and Systems
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• v.7 no.2
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• pp.117-132
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• 2011

This paper is aimed at presenting two methods on the basis of pattern search and genetic algorithms to detect and estimate damage in plates using the modal data of a damaged plate. The proposed methods determine the damages of plate structures using optimization of an objective function by pattern search and genetic algorithms. These methods have been applied to two numerical examples, namely four-fixed supported and cantilever plates with and without noise in the modal data and containing one or several damages. The obtained results clearly reveal that the proposed methods can be viewed as a powerful and reliable method for structural damage detection in plates using the modal data.

• Journal of Genetic Medicine
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• v.13 no.1
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• pp.1-13
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• 2016

Although some mutations are beneficial and are the driving force behind evolution, it is important to maintain DNA integrity and stability because it contains genetic information. However, in the oxygen-rich environment we live in, the DNA molecule is under constant threat from endogenous or exogenous insults. DNA damage could trigger the DNA damage response (DDR), which involves DNA repair, the regulation of cell cycle checkpoints, and the induction of programmed cell death or senescence. Dysregulation of these physiological responses to DNA damage causes developmental defects, neurological defects, premature aging, infertility, immune system defects, and tumors in humans. Some human syndromes are characterized by unique neurological phenotypes including microcephaly, mental retardation, ataxia, neurodegeneration, and neuropathy, suggesting a direct link between genomic instability resulting from defective DDR and neuropathology. In this review, rare human genetic disorders related to abnormal DDR and damage repair with neural defects will be discussed.

• Structural Engineering and Mechanics
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• v.30 no.2
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• pp.135-146
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• 2008

This study utilizes the fine-tuning and small-digit characteristics of the successive zooming genetic algorithm (SZGA) to propose a method of structural damage detection in a continuum structure, where the differences in the natural frequencies of a structure obtained by experiment and FEM are compared and minimized using an assumed location and extent of structural damage. The final methodology applied to the structural damage detection is a kind of pseudo-discrete-variable-algorithm that counts the soundness variables as one (perfectly sound) if they are above a certain standard, such as 0.99. This methodology is based on the fact that most well-designed structures exhibit failures at some critical point due to manufacturing error, while the remaining region is free of damage. Thus, damage of 1% (depending on the given standard) or less can be neglected, and the search concentrated on finding more serious failures. It is shown that the proposed method can find out the exact structural damage of the monitored structure and reduce the time and amount of computation.

• Structural Engineering and Mechanics
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• v.42 no.3
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• pp.425-448
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• 2012

In this paper, a new damage detection and quantification method has been presented to perform detection and quantification of structural damage under ambient vibration loadings. To extract modal properties of the structural system under ambient excitation, natural excitation technique (NExT) and eigensystem realization algorithm (ERA) are employed. Sensitivity matrices of the dynamic residual force vector have been derived and used in the parameter subset selection method to identify multiple damaged locations. In the sequel, the steady state genetic algorithm (SSGA) is used to determine quantified levels of the identified damage by minimizing errors in the modal flexibility matrix. In this study, performance of the proposed damage detection and quantification methodology is evaluated using a finite element model of a truss structure with considerations of possible experimental errors and noises. A series of numerical examples with five different damage scenarios including a challengingly small damage level demonstrates that the proposed methodology can efficaciously detect and quantify damage under noisy ambient vibrations.

• Steel and Composite Structures
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• v.25 no.4
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• pp.485-496
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• 2017

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.

• Smart Structures and Systems
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• v.15 no.1
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• pp.227-244
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• 2015

This study develops a two stage procedure to identify the structural damage based on the optimized artificial neural networks. Initially, the modal strain energy index (MSEI) is established to extract the damaged elements and to reduce the computational time. Then the genetic algorithm (GA) and artificial neural networks (ANNs) are combined to detect the damage severity. The input of the network is modal strain energy index and the output is the flexural stiffness of the beam elements. The principal component analysis (PCA) is utilized to reduce the input variants of the neural network. By using the genetic algorithm to optimize the parameters, the ANNs can significantly improve the accuracy and convergence of the damage identification. The influence of noise on damage identification results is also studied. The simulation and experiment on beam structures shows that the adaptive parameter selection neural network can identify the damage location and severity of beam structures with high accuracy.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.211-219
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• 2023

The structural health of a pipeline is usually assessed by visual inspection. In addition to the fact that this method is expensive and time consuming, inspection of the whole structure is not possible due to limited access to some points. Therefore, adopting a damage detection method without the mentioned limitations is important in order to increase the safety of the structure. In recent years, vibration-based methods have been used to detect damage. These methods detect structural defects based on the fact that the dynamic responses of the structure will change due to damage existence. Therefore, the location and extent of damage, before and after the damage, are determined. In this study, fuzzy genetic algorithm has been used to monitor the structural health of the pipeline to create a fuzzy automated system and all kinds of possible failure scenarios that can occur for the structure. For this purpose, the results of an experimental model have been used. Its numerical model is generated in ABAQUS software and the results of the analysis are used in the fuzzy genetic algorithm. Results show that the system is more accurate in detecting high-intensity damages, and the use of higher frequency modes helps to increase accuracy. Moreover, the system considers the damage in symmetric regions with the same degree of membership. To deal with the uncertainties, some error values are added, which are observed to be negligible up to 10% of the error.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.1
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• pp.1-11
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• 2008

Stiffness estimation of a shear building due to local damages is usually achieved though structural analysis based on the assumed material properties and idealized numerical modeling of structure. Conventional numerical modeling, however, frequently causes an inevitable error in the structural response and this makes it difficult to exactly predict the damage state in structure. To solve this problem, this paper introduces a damage detection technique for shear building using genetic algorithm. The introduced algorithm evaluates the damage in structure using a flexibility matrix since the flexibility matrix can exactly be obtained from the field test in spite of using a few lower dynamic modes of structure. The introduced algorithm is expected to be more effectively used in damage detection of structures rather than conventional method using the stiffness matrix. Moreover, even in cases when an accurate measurement of structural stiffness cannot be expected, the proposed technique makes it possible to estimate the absolute change in stiffness of the structure on the basis of genetic algorithm. The validity of the proposed technique is demonstrated though numerical analysis using OPENSEES.