• Structural Engineering and Mechanics
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• v.25 no.2
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• pp.131-146
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• 2007

This paper presents the results of a study to show how the development of a crack alters the structural behavior of I-shaped steel beams and how this can be used to evaluate nondestructive evaluation techniques. The approach is based on changes in the dynamic behavior. An approximate finite element model for a cracked beam with I-shaped cross-section is developed based on a simplified fracture model. The model is then used to review different damage cases. Damage detection techniques are studied to determine their ability to identify the existence of the crack and to identify its location. The techniques studied are the coordinate modal assurance criterion, the modal flexibility, and the state and the slope arrays.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.2
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• pp.128-134
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• 2014

Infrared thermography is an emerging approach to non-contact, non-intrusive, and non-destructive inspection of various solid materials such as metals, composites, and semiconductors for industrial and research interests. In this study, data processing was applied to infrared thermography measurements to detect defects in metals that were widely used in industrial fields. When analyzing experimental data from infrared thermographic testing, raw images were often not appropriate. Thus, various data analysis methods were used at the pre-processing and processing levels in data processing programs for quantitative analysis of defect detection and characterization; these increased the infrared non-destructive testing capabilities since subtle defects signature became apparent. A 3D finite element simulation was performed to verify and analyze the data obtained from both the experiment and the image processing techniques.

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.21-28
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• 2015

In this paper, we propose a method of glasses detection in facial image. we develop a detection method of glasses with a weighted sum of the results that detected by facial element detection and glasses frame candidate region. Component of the face detection method detects the glasses, by defining the detection probability of the glasses according to the detection of a face component. Method using the candidate region of the glasses frame detects the glasses, by defining feature of the glasses frame in the candidate region. finally, The results of the combined weight of both methods are obtained. The proposed method in this paper is expected to increase security system's recognition on facial accessories by raising detection performance of glasses or sunglasses for using ATM.

• Journal of Electrochemical Science and Technology
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• v.10 no.2
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• pp.206-213
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• 2019

The development of simple methods for As detection has received great attention because As is a toxic chemical element causing environmental and health-related issues. In this work, the effect of nanostructures of Au electrodes on their electroanalytical performance during As detection was investigated. Different Au nanostructures with various surface morphologies such as nanoplate Au, nanospike Au, and dendritic Au structures were prepared, and their electrochemical behaviors toward square-wave anodic stripping voltammetric As detection were examined. The difference in intrinsic efficiency for As detection between nanostructured and flat Au electrodes was explained based on the crystallographic orientations of Au surfaces, as examined by the underpotential deposition of Pb. The most efficient As detection performance was obtained with nanoplate Au electrodes, and the effects of the pre-deposition time and interference on As detection of the nanoplate Au electrodes were also investigated.

• Smart Structures and Systems
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• v.20 no.4
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• pp.473-481
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• 2017

This study suggests a simple, convenient and non-destructive method for investigation of the Young's modulus detection in stepped shafts which only utilizes the first-order resonant frequency in flexural mode and dimensions of structures. The method is based on the impulse excitation technique (IET) to pick up the fundamental resonant frequencies. The standard Young's modulus detection formulas for rectangular and circular cross-sections are well investigated in literatures. However, the Young's modulus of stepped shafts can not be directly detected using the formula for a beam with rectangular or circular cross-section. A response surface method (RSM) is introduced to design numerical simulation experiments to build up experimental formula to detect Young's modulus of stepped shafts. The numerical simulation performed by finite element method (FEM) to obtain enough simulation data for RSM analysis. After analysis and calculation, the relationship of flexural resonant frequencies, dimensions of stepped shafts and Young's modulus is obtained. Numerical simulations and experimental investigations show that the IET method can be used to investigate Young's modulus in stepped shafts, and the FEM simulation and RSM based IET formula proposed in this paper is applicable to calculate the Young's modulus in stepped shaft. The method can be further developed to detect mechanical parameters of more complicated structures using the combination of FEM simulation and RSM.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.11
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• pp.1299-1307
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• 2014

In this work, an appropriate analysis model of a precise foot sensor with low detection pressure capability under a low range of variation in the dimensional variables was proposed. With a simple two-dimensional model, it was found that a remarkably high error level sometimes occurred between the analysis and experimental results. In order to overcome the error and improve the performance, a three-dimensional model was introduced, and the detection pressure and sensor characteristics were compared with those of the experimental results, which showed its enhanced performance with less error and higher precision.

• Structural Engineering and Mechanics
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• v.54 no.6
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• pp.1217-1244
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• 2015

In this research a theoretical and numerical study on a bridge damage detection procedure is presented based on vibration measurements collected from a set of accelerometers. This method, referred to as "Adjoint Variable Method", is a sensitivity-based finite element model updating method. The approach relies on minimizing a penalty function, which usually consists of the errors between the measured quantities and the corresponding predictions attained from the model. Moving mass is an interactive model and includes inertia effects between the model and mass. This interactive model is a time varying system and the proposed method is capable of detecting damage in this variable system. Robustness of the proposed method is illustrated by correct detection of the location and extension of predetermined single, multiple and random damages in all ranges of speed and mass ratio of moving vehicle. A comparative study on common sensitivity and the proposed method confirms its efficiency and performance improvement in sensitivity-based damage detection methods. In addition various possible sources of error, including the effects of measurement noise and initial assumption error in stability of method are also discussed.

• Smart Structures and Systems
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• v.5 no.6
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• pp.681-697
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• 2009

Limited, noisy data in vibration testing is a hindrance to the development of structural damage detection. This paper presents a method for optimizing sensor placement and performing damage detection using finite element model updating. Sensitivity analysis of the modal flexibility matrix determines the optimal sensor locations for collecting information on structural damage. The optimal sensor locations require the instrumentation of only a limited number of degrees of freedom. Using noisy modal data from only these limited sensor locations, a method based on model updating and changes in the flexibility matrix successfully determines the location and severity of the imposed damage in numerical simulations. In addition, a steel cantilever beam experiment performed in the laboratory that considered the effects of model error and noise tested the validity of the method. The results show that the proposed approach effectively and robustly detects structural damage using limited, optimal sensor information.

• Structural Engineering and Mechanics
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• v.36 no.5
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• pp.643-667
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• 2010

The classical flexibility difference method detects damage by observing the difference of conventional deflection flexibility matrices between pre- and post-damaged states of a structure. This method is not able to identify multiple damage scenarios, and its criteria to identify damage depend upon the boundary conditions of structures. The key point behind the inability and dependence is revealed in this study. A more feasible flexibility for damage detection, the Angle-between-String-and-Horizon (ASH) flexibility, is proposed. The physical meaning of the new flexibility is given, and synthesis of the new flexibility matrix by modal frequencies and translational mode shapes is formulated. The damage indicators are extracted from the difference of ASH flexibility matrices between the pre- and post-damaged structures. One feature of the ASH flexibility is that the components in the ASH flexibility matrix are associated with elements instead of Nodes or DOFs. Therefore, the damage indicators based on the ASH flexibility are mapped to structural elements directly, and thus they can pinpoint the damaged elements, which is appealing to damage detection for complex structures. In addition, the change in the ASH flexibility caused by damage is not affected by boundary conditions, which simplifies the criteria to identify damage. Moreover, the proposed method can determine relatively the damage severity. Because the proposed damage indicator of an element mainly reflects the deflection change within the element itself, which significantly reduces the influence of the damage in one element on the damage indicators of other damaged elements, the proposed method can identify multiple damage locations. The viability of the proposed approach has been demonstrated by numerical examples and experimental tests on a cantilever beam and a simply supported beam.

• Steel and Composite Structures
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• v.46 no.3
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• pp.319-334
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• 2023

Localizing damages is an essential task to monitor the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Backpropagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions in thin, flat, and folded structures having 15⁰, 30⁰, 45⁰, and 60⁰ folding angle have been modeled and subjected to free vibration analysis by employing the Classical Plate Theory with Finite Element Method. A Four-nodded quadrilateral element having six degrees of freedom has been considered to represent those structures mathematically. The first ten natural frequencies have been obtained regarding healthy and cracked structures. To localize the crack, the ratios of the frequencies of the cracked flat and folded structures to those of healthy ones have been taken into account. Those ratios have been given to BPANN as the input variables, while the crack locations have been considered as the output variables. A total of 500 crack locations have been regarded within the dataset obtained from the results of the free vibration analysis. To build the best intelligent model, a feature search has been conducted for BAPNN regarding activation function, the number of hidden layers, and the number of hidden neurons. Regarding the analysis results, it is concluded that the BPANN is able to localize the cracks with an average accuracy of 95.12%.