• Structural Engineering and Mechanics
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• v.75 no.5
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• pp.585-594
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• 2020

Detecting the damage of indeterminate trusses is of major importance in the literature. This paper proposes a quick approach in this regard, utilizing a precise mathematical approach based on Finite Element Method. Different to a general two-step method defined in the literature essentially based on optimization approach, this method consists of three steps including Damage-Suspected Element Identification step, Imminent Damaged Element Identification step, and finally, Damage Severity Detection step and does not need any optimizing algorithm. The first step focuses on the identification of damage-suspected elements using an index based on modal residual force vector. In the second step, imminent damage elements are identified among the damage-suspected elements detected in the previous step using a specific technique. Ultimately, in the third step, a novel relation is derived to calculate the damage severity of each imminent damaged element. To show the efficiency and quick function of the proposed method, three examples including a 25-bar planar truss, a 31-bar planar truss, and a 52-bar space truss are studied; results of which indicate that the method is innovatively capable of suitably detecting, for indeterminate trusses, not only damaged elements but also their individual damage severity by carrying out solely one analysis.

• Structural Engineering and Mechanics
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• v.67 no.5
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• pp.457-464
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• 2018

This paper presents a method for detecting damage in irregular 2D and 3D continuum structures based on combination of wavelet transform (WT) with fuzzy inference system (FIS) and particle swarm optimization (PSO). Many damage detection methods study regular structures. This method studies irregular structures and doesn't need response of healthy structures. First the damaged structure is analyzed with finite element methods, and damage response is obtained at the finite element points that have irregular distance, secondly the FIS, which is optimized by PSO is used to obtain responses at points, having equal distance by response at those points that previously obtained by the finite element methods. Then a 2D (for 2D continuum structures) or a 3D (for 3D continuum structures) matrix is performed by equal distance point response. Thirdly, by applying 2D or 3D wavelet transform on 2D or 3D matrix that previously obtained by FIS detail matrix coefficient of WT is obtained. It is shown that detail matrix coefficient can determine the damage zone of the structure by perturbation in the damaged area. In order to illustrate the capability of proposed method some examples are considered.

• Computers and Concrete
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• v.13 no.2
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• pp.209-220
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• 2014

Vibration based damage detection is very popular in the civil engineering area. Especially, special structures like dams, long-span bridges and high-rise buildings, need continues monitoring in terms of mechanical properties of material, static and dynamic behavior. It has been stated in the International Commission on Large Dams that more than half of the large concrete dams were constructed more than 50 years ago and the old dams have subjected to repeating loads such as earthquake, overflow, blast, etc.,. So, some unexpected failures may occur and catastrophic damages may be taken place because of theloss of strength, stiffness and other physical properties of concrete. Therefore, these dams need repairs provided with global damage evaluation in order to preserve structural integrity. The paper aims to show the effectiveness of the model updating method for global damage detection on a laboratory arch dam model. Ambient vibration test is used in order to determine the experimental dynamic characteristics. The initial finite element model is updated according to the experimentally determined natural frequencies and mode shapes. The web thickness is selected as updating parameter in the damage evaluation. It is observed from the study that the damage case is revealed with high accuracy and a good match is attained between the estimated and the real damage cases by model updating method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.4
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• pp.137-147
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• 1998

The neural net application was tried to develop the technique for monitoring the health status of a steel truss bridge which was scaled down to 1/15 of the real bridge for the laboratory experiments. The damage scenarios were chosen as 7 cases. The dynamic behavior, which was changed due to the breakage of the members, of the bridge was investigated by finite element analysis. The bridge consists of single spam, and eight (8) main structural subsystems. The loading vehicle, which weighs as 100 kgf, was operated by the servo-motor controller. The accelerometers were bonded on the surface of 7 cross-beams to measure the dynamic behavior induced by the abnormal structural condition. Artificial neural network technique was used to determine the severity of the damage. At first, the neural net was learnt by the results of finite element analysis, and also, the maximum detection error was 3.65 percents. Another neural net was also learnt, and verified by the experimental results, and in this case, the maximum detection error was 1.05 percents. In future study, neural net is necessary to be learnt and verified by various data from the real bridge.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.693-700
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• 2006

To detect and evaluate the damage present in bridge, static identification method is known to be simple and effective, compared to dynamic method. In this study, the damage detection method in steel box girder bridge using static responses including displacement, slope and curvature is examined. The static displacement is calculated using finite element analysis and the slope and curvature are determined from the displacement using central difference method. The location of damage is detected using the absolute differences of these responses in intact and damaged bridge. Steel box girder bridge with corner crack is modeled using singular element in finite element method. The results show that these responses were significantly useful in detecting and predicting the location of damage present in bridge.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.953-964
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• 2020

Ever since media contents took over the world, the importance of typography has increased, and the influence of fonts has be n recognized. Nevertheless, the current Hangul font system is very poor and is provided passively, so it is practically impossible to understand and utilize all the shape characteristics of more than six thousand Hangul fonts. In this paper, the characteristics of Hangul font shapes were selected based on the Hangul structure of similar fonts. The stroke element detection training was performed by fine tuning Faster R-CNN Inception v2, one of the deep learning object detection models. We also propose a system that automatically extracts the stroke element characteristics from characters by introducing an automatic extraction algorithm. In comparison to the previous research which showed poor accuracy while using SVM(Support Vector Machine) and Sliding Window Algorithm, the proposed system in this paper has shown the result of 10 % accuracy to properly detect and extract stroke elements from various fonts. In conclusion, if the stroke element characteristics based on the Hangul structural information extracted through the system are used for similar classification, problems such as copyright will be solved in an era when typography's competitiveness becomes stronger, and an automated process will be provided to users for more convenience.

• Geomechanics and Engineering
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• v.12 no.1
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• pp.161-183
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• 2017

This paper investigates the damage identification of the concrete pile element through axial wave propagation technique using computational and experimental studies. Now-a-days, concrete pile foundations are often common in all engineering structures and their safety is significant for preventing the failure. Damage detection and estimation in a sub-structure is challenging as the visual picture of the sub-structure and its condition is not well known and the state of the structure or foundation can be inferred only through its static and dynamic response. The concept of wave propagation involves dynamic impedance and whenever a wave encounters a changing impedance (due to loss of stiffness), a reflecting wave is generated with the total strain energy forked as reflected as well as refracted portions. Among many frequency domain methods, the Spectral Finite Element method (SFEM) has been found suitable for analysis of wave propagation in real engineering structures as the formulation is based on dynamic equilibrium under harmonic steady state excitation. The feasibility of the axial wave propagation technique is studied through numerical simulations using Elementary rod theory and higher order Love rod theory under SFEM and ABAQUS dynamic explicit analysis with experimental validation exercise. Towards simulating the damage scenario in a pile element, dis-continuity (impedance mismatch) is induced by varying its cross-sectional area along its length. Both experimental and computational investigations are performed under pulse-echo and pitch-catch configuration methods. Analytical and experimental results are in good agreement.

• Smart Structures and Systems
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• v.19 no.1
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• pp.11-20
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• 2017

For structural damage detection of shear buildings, this paper proposes a new concept using structural element mass-stiffness vector (SEMV) based on special mass and stiffness distribution characteristics. A corresponding damage identification method is developed combining the SEMV with the cross-model cross-mode (CMCM) model updating algorithm. For a shear building, a model is assumed at the beginning based on the building's distribution characteristics. The model is updated into two models corresponding to the healthy and damaged conditions, respectively, using the CMCM method according to the modal parameters of actual structure identified from the measured acceleration signals. Subsequently, the structural SEMV for each condition can be calculated from the updated model using the corresponding stiffness and mass correction factors, and then is utilized to form a new feature vector in which each element is calculated by dividing one element of SEMV in health condition by the corresponding element of SEMV in damage condition. Thus this vector can be viewed as a damage detection feature for its ability to identify the mass or stiffness variation between the healthy and damaged conditions. Finally, a numerical simulation and the laboratory experimental data from a test-bed structure at the Los Alamos National Laboratory were analyzed to verify the effectiveness and reliability of the proposed method. Both simulated and experimental results show that the proposed approach is able to detect the presence of structural mass and stiffness variation and to quantify the level of such changes.

• Computational Structural Engineering
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• v.9 no.4
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• pp.155-163
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• 1996

In the analysis of thin elastic structures such as beam-, arch-, plate- and shell-like bodies using standard finite element schemes, there may occur deterioration of approximation quality owing to shear and membrane lockings. Moreover, a recognition of this phenomenon in the computed numerical results is not easy without comparing with other available reference numerical data. This paper analyses briefly this phenomenon and introduces one inexpensive but reliable a posteriori locking detection method. Numerical examples are given supporting the theoretical results.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.324-328
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• 2007

Vibration monitoring of rolling element bearings is probably the most established diagnostic technique for rotating machinery. Acoustic Emission (AE) Analysis is an extremely powerful technology that can be used within a wide range of applications of non destructive testing. Therefor, this paper investigates the detection methods using AE for rolling element bearings about low-speed. Two transducers, the accelerometer and acoustic emission sensor, are used to acquire data and the results are compared for the capacity of early fault detection.