• Smart Structures and Systems
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• 제19권2호
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• pp.127-136
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• 2017

Fiber reinforced polymer (FRP) has received widespread attention in the field of civil engineering because of its superior durability and corrosion resistance. This article presents the damage mechanisms of a novelty composite called carbon nanofiber modified flax fiber polymer (CNF-modified FFRP). The ability of acoustic emission (AE) to detect damage evolution for different configurations of specimens under uniaxial tension was examined, and some useful AE characteristic parameters were obtained. Test results shows that the mechanical properties of modified composites are associated with the CNF content and the evenness of CNF dispersed in the epoxy matrix. Various damage mechanisms was established by means of scanning electron microscope images. The fuzzy c-means clustering were proposed to classify AE events into groups representing different generation mechanisms. The classifiers are constructed using the traditional AE features -- six parameters from each burst. Amplitude and peak-frequency were selected as the best cluster-definition features from these AE parameters. After comprehensive comparison, a correlation between these AE events classes and the damage mechanisms observed was proposed.

• 한국소음진동공학회논문집
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• 제16권2호
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• pp.115-123
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• 2006

A feasibility of using the topology optimization method for structural damage identification is investigated for the first time. The frequency response functions (FRFs) are assumed to be constructed by the finite element models of damaged and undamaged structures. In addition to commonly used resonances, antiresonances are employed as the damage identifying modal parameters. For the topology optimization formulation, the modal parameters of the undamaged structure are made to approach those of the damaged structure by means of the constraint equations, while the objective function is an explicit penalty function requiring clear black-and-white images. The developed formulation is especially suitable for damage identification problems dealing with many modal parameters. Although relatively simple numerical problems were considered in this investigation, the possibility of using the topology optimization method for structural damage identification is suggested through this research.

• Journal of Mechanical Science and Technology
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• 제19권7호
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• pp.1393-1404
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• 2005

Fatigue crack growth and life have been estimated based on established empirical equations. In this paper, an alternative method using artificial neural network (ANN) -based model developed to predict fatigue damages simultaneously. To learn and generalize the ANN, fatigue crack growth rate and life data were built up using in-plane bending fatigue test results. Single fracture mechanical parameter or nondestructive parameter can't predict fatigue damage accurately but multiple fracture mechanical parameters or nondestructive parameters can. Existing fatigue damage modeling used this merit but limited real-time damage monitoring. Therefore, this study shows fatigue damage model using backpropagation neural networks on the basis of X -ray half breadth ratio B / $B_o$, fractal dimension $D_f$ and fracture mechanical parameters can estimate fatigue crack growth rate da/ dN and cycle ratio N / $N_f$ at the same time within engineering limit error ($5\%$).

• Smart Structures and Systems
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• 제15권1호
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• pp.57-80
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• 2015

Extended Kalman Filter (EKF) has been widely used for structural identification and damage detection. However, conventional EKF approaches require that external excitations are measured. Also, in the conventional EKF, unknown structural parameters are included as an augmented vector in forming the extended state vector. Hence the sizes of extended state vector and state equation are quite large, which suffers from not only large computational effort but also convergence problem for the identification of a large number of unknown parameters. Moreover, such approaches are not suitable for intelligent structural damage detection due to the limited computational power and storage capacities of smart sensors. In this paper, a two-stage and two-step algorithm is proposed for the identification of structural damage as well as unknown external excitations. In stage-one, structural state vector and unknown structural parameters are recursively estimated in a two-step Kalman estimator approach. Then, the unknown external excitations are estimated sequentially by least-squares estimation in stage-two. Therefore, the number of unknown variables to be estimated in each step is reduced and the identification of structural system and unknown excitation are conducted sequentially, which simplify the identification problem and reduces computational efforts significantly. Both numerical simulation examples and lab experimental tests are used to validate the proposed algorithm for the identification of structural damage as well as unknown excitations for structural health monitoring.

• Steel and Composite Structures
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• 제14권4호
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• pp.367-377
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• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• 한국해양공학회지
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• 제30권5호
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• pp.341-348
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• 2016

The fatigue damage caused by wide band loadings has generally been predicted using fatigue damage models in the frequency domain rather than a rain-flow counting method in the time domain because of its computation cost. This study showed that these fatigue damage models can be simplified in the form of normalized fatigue damage as a function of the S-N curve slope and bandwidth parameters. Based on numerical simulations of various wide band spectra, it was found that fatigue damage models in the form of normalized fatigue damage with one S-N curve slope and two bandwidth parameters( α₁ , α₂ ) provided less reasonable fatigue damage. Therefore, an additional bandwidth parameter needs to be considered based on a sensitivity study using various neural networks, which proved that α_1-5 would be the dominant factor of a fatigue damage model as an additional bandwidth parameter.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2053-2068
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• 2023

Following a loss of coolant accident (LOCA), prestressing concrete containment vessels (PCCVs) may experience high thermal load as well as internal pressure. The high temperature stress would increase the risk of premature damage to the containment, which reduces the safety margin during the increasing internal pressure. However, current investigations cannot clearly address the issues of thermal-pressure coupling effect on damage propagation and thus safety of the containment. Thus, this paper offers three simple and powerful damage parameters to differentiate the severity of damage of the containment. Moreover, despite of the temperature action severely threatening the pressure performance of the containment, the research regarding the improvement of the resistant performance of the containment is quite scarce. Therefore, in this paper, a comprehensive comparison of damage propagation and mechanism between conventional and fiber-reinforced concrete (FRC) containments is performed. The effects of fiber characteristics parameters on damage propagation of structures following the LOCA are also specifically revealed. It is found that the proposed damage indices can properly indicate state of damage in the containment body and the addition of fiber can be used to obviously mitigate the damage propagation in PCCV considering the thermal-pressure coupling.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2006년도 춘계학술대회 논문집
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• pp.254-257
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• 2006

In the last few years, ductile fracture criteria based on various hypotheses have been developed and utilized with FEM to predict forming failure. The accurate deformation analysis by the FEM and the decision of damage parameters are the most important factors in these approaches. In this paper, several conventional integral forms of fracture criteria were introduced and the test method to determine damage parameters by using notched specimen was suggested. Based on the results, damage parameters obtained under the different stress system (tensile and compression) are compared and analyzed.

• 한국정밀공학회지
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• 제16권7호
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• pp.168-177
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• 1999

Fatigue damage is the phenomena which is accumulated gradually with loading cycle in material. It is represented by fatigue crack growth rate da/dN and fatigue life ratio $N/N_{f}$. Fracture mechanical parameters estimating large crack growth behavior can calculate quantitative amount of fatigue crack growth resistance in engineering material. But fatigue damage has influence on various load, material and environment. Therefore, In this study, we propose that artificial intelligent fatigue damage model can predicts fatigue crack growth rate da/dN and fatigue life ratio $N/N_{f}$ simultaneously using fracture mechanical and nondestructive parameters.

• Structural Engineering and Mechanics
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• 제26권2호
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• pp.179-190
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• 2007

Damage detection based on measured vibration data has received intensive studies recently. Frequently, the damage to a structure may be reflected by a change of some system parameters, such as a degradation of the stiffness. In this paper, we apply a method to nondestructively locate and estimate the severity of damage in corrosion pipeline for which a few natural frequencies or mode shapes are available. The method is based on the strain modal sensitivity ratio (SMSR) and the orthogonality conditions sensitivities (OCS) applied to vibration features identified during the monitoring of the pipeline. The advantage of these methods is that it only requires measuring few modal parameters. The SMSR-based and OCS-based damage detection methods are illustrated using computer-simulated and laboratory testing data. The results show that the current method provides a precise indication of both the location and the extent of corrosion pipeline.