• 한국기계가공학회지
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• 제11권4호
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• pp.72-81
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• 2012

This study investigates structural, fatigue and modal analyses at bicycle suspension seat post. When weight is applied to the saddle, models 1 and 2 have the weakest strength at the part connected with saddle. And model 2 is greater total deformation and equivalent stress than model 1. Among the cases of nonuniform fatigue loads at models 1 and 2, 'SAE bracket history' with the severest change of load becomes most unstable but 'Sample history' becomes most stable. In case of 'Sample history' with the average stress of 0 to $-10^4MPa$ and the amplitude stress of 0 to $10^4MPa$, the possibility of maximum damage becomes 4%. This stress state can be shown with 5 to 7times more than the damage possibility of 'SAE bracket history' or 'SAE transmission'. Model 1 has better impulse relaxation and passenger sensitivity than model 2. The structural result of this study can be effectively utilized with the design of bicycle suspension seat post by investigating prevention and durability against its damage.

• Structural Engineering and Mechanics
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• 제72권2호
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• pp.191-201
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• 2019

Finite element analysis is one of the most used tools for studying femoral neck fracture. Nerveless, consensus concerning either the choice of material characteristics, damage law and /or geometric models (linear on nonlinear) remains unreached. In this work, we propose a numerical quasi-brittle damage model to describe the behavior of the proximal femur associated with two methods to evaluate the Young modulus. Eight proximal femur finite elements models were constructed from CT scan data (4 donors: 3 women; 1 man). The numerical computations showed a good agreement between the numerical curves (load - displacement) and the experimental ones. A very encouraging result is obtained when a comparison is made between the computed fracture loads and the experimental ones ($R^2=0.825$, Relative error =6.49%). All specific numerical computation provided very fair qualitative matches with the fracture patterns for the sideway fall simulation. Finally, the comparative study based on 32 simulations adopting linear and nonlinear meshing led to the conclusion that the quantitatively results are improved when a nonlinear mesh is used.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3229-3240
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• 2023

Due to neutron radiation, atomic displacement has a significant effect on material in nuclear reactors. A range of secondary displacement models, including the Kinchin-Pease (K-P), Lindhard, Norgett-Robinson-Torrens (NRT), and athermal recombination-corrected displacement per atom (arc-dpa) have been suggested to calculate the number of displacement per atom (dpa). As neutron elastic interaction is the main cause of displacement damage, the focus of the current study is to calculate the atomic displacement caused by the neutron elastic interaction in order to estimate the exact amount of yield strength in a WWER-1000 reactor pressure vessel. To achieve this purpose, the reactor core is simulated by MCNPX code. In addition, a program is developed to calculate the elastic radiation damage induced by the incident neutron flux (RADIX) based on different models using Fortran programming language. Also, due to non-elastic interaction, the displacement damage is calculated by the HEATR module of the NJOY code. ASME E-693-01 standard, SPECTER, NJOY codes, and other pervious findings have been used to validate RADIX results. The results showed that the RADIX(arc-dpa)/HEATR outputs have appropriate accuracy. The relative error of the calculated dpa resulting from RADIX(arc-dpa)/HEATR is about 8% and 46% less than NJOY code, respectively in the ¼ and ¾ vessel wall.

• Steel and Composite Structures
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• 제49권1호
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• pp.65-79
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• 2023

This paper presents a development of empirical evaluations, which can be used to evaluate the damage of fiber-reinforced concrete composites (FRC) wall subjected to close-in blast loads. For this development, a combined application of numerical simulation and machine learning approaches are employed. First, finite element modeling of FRC wall under blast loading is developed and verified using experimental data. Numerical analyses are then carried out to investigate the dynamic behavior of the FRC wall under blast loading. In addition, a data set of 384 samples on the damage of FRC wall due to blast loads is then produced in order to develop machine learning models. Second, three robust machine learning models of Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) are employed to propose empirical evaluations for predicting the damage of FRC wall. The proposed empirical evaluations are very useful for practical evaluation and design of FRC wall subjected to blast loads.

• Nuclear Engineering and Technology
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• 제56권2호
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• pp.688-706
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• 2024

Austenitic stainless steels (ASS) are extensively employed in various sectors such as nuclear, power, petrochemical, oil and gas because of their excellent structural strength and resistance to corrosion. SS304 and SS316 are the predominant choices for piping, pressure vessels, heat exchangers, nuclear reactor core components and support structures, but they are susceptible to stress corrosion cracking (SCC) in chloride-rich environments. Over the course of several decades, extensive research efforts have been directed towards evaluating SCC using diverse methodologies and models, albeit some uncertainties persist regarding the precise progression of cracks. This review paper focuses on the application of Acoustic Emission Technique (AET) for assessing SCC damage mechanism by monitoring the dynamic acoustic emissions or inelastic stress waves generated during the initiation and propagation of cracks. AET serves as a valuable non-destructive technique (NDT) for in-service evaluation of the structural integrity within operational conditions and early detection of critical flaws. By leveraging the time domain and time-frequency domain techniques, various Acoustic Emission (AE) parameters can be characterized and correlated with the multi-stage crack damage phenomena. Further theories of the SCC mechanisms are elucidated, with a focus on both the dissolution-based and cleavage-based damage models. Through the comprehensive insights provided here, this review stands to contribute to an enhanced understanding of SCC damage in stainless steels and the potential AET application in nuclear industry.

• Smart Structures and Systems
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• 제20권2호
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• pp.207-217
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• 2017

Because of the inevitable uncertainties such as structural parameters, external excitations and measurement noises, the effects of uncertainties should be taken into consideration in structural damage detection. In this paper, two probabilistic structural damage detection approaches are proposed to account for the underlying uncertainties in structural parameters and external excitation. The first approach adopts the statistical moment-based structural damage detection (SMBDD) algorithm together with the sensitivity analysis of the damage vector to the uncertain parameters. The approach takes the advantage of the strength SMBDD, so it is robust to measurement noise. However, it requests the number of measured responses is not less than that of unknown structural parameters. To reduce the number of measurements requested by the SMBDD algorithm, another probabilistic structural damage detection approach is proposed. It is based on the integration of structural damage detection using temporal moments in each time segment of measured response time history with the sensitivity analysis of the damage vector to the uncertain parameters. In both approaches, probability distribution of damage vector is estimated from those of uncertain parameters based on stochastic finite element model updating and probabilistic propagation. By comparing the two probability distribution characteristics for the undamaged and damaged models, probability of damage existence and damage extent at structural element level can be detected. Some numerical examples are used to demonstrate the performances of the two proposed approaches, respectively.

• Structural Engineering and Mechanics
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• 제35권6호
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• pp.735-758
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• 2010

The paper proposes two methodologies for damage identification from measured natural frequencies of a contiguously damaged reinforced concrete beam, idealised with distributed damage model. The first method identifies damage from Iso-Eigen-Value-Change contours, plotted between pairs of different frequencies. The performance of the method is checked for a wide variation of damage positions and extents. The method is also extended to a discrete structure in the form of a five-storied shear building and the simplicity of the method is demonstrated. The second method is through smeared damage model, where the damage is assumed constant for different segments of the beam and the lengths and centres of these segments are the known inputs. First-order perturbation method is used to derive the relevant expressions. Both these methods are based on distributed damage models and have been checked with experimental program on simply supported reinforced concrete beams, subjected to different stages of symmetric and un-symmetric damages. The results of the experiments are encouraging and show that both the methods can be adopted together in a damage identification scenario.

• Smart Structures and Systems
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• 제14권2호
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• 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.

• Structural Engineering and Mechanics
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• 제17권5호
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• pp.655-670
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• 2004

This paper aims to evaluate the effect of typhoons on fatigue damage accumulation in steel decks of long-span suspension bridges. The strain-time histories at critical locations of deck sections of long-span bridges during different typhoons passing the bridge area are investigated by using on-line strain data acquired from the structural health monitoring system installed on the bridge. The fatigue damage models based on Miner's Law and Continuum Damage Mechanics (CDM) are applied to calculate the increment of fatigue damage due to the action of a typhoon. Accumulated fatigue damage during the typhoon is also calculated and compared between Miner's Law and the CDM method. It is found that for the Tsing Ma Bridge case, the stress spectrum generated by a typhoon is significantly different than that generated by normal traffic and its histogram shapes can be described approximately as a Rayleigh distribution. The influence of typhoon loading on accumulative fatigue damage is more significant than that due to normal traffic loading. The increment of fatigue damage generated by hourly stress spectrum for the maximum typhoon loading may be much greater than those for normal traffic loading. It is, therefore, concluded that it is necessary to evaluate typhoon induced fatigue damage for the purpose of accurately evaluating accumulative fatigue damage for long-span bridges located within typhoon prone regions.