• Structural Engineering and Mechanics
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• 제54권5호
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• pp.855-875
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• 2015

Many novel materials exhibit a property of different elastic moduli in tension and compression. One such material is graphene, a wonder material, which has the highest strength yet measured. Investigations on buckling problems for structures with different moduli are scarce. To address this new problem, firstly, the nondimensional expression of the relation between offset of neutral axis and deflection curve is derived based on the phased integration method, and then using the energy method, load-deflection relation of the rod is determined; Secondly, based on the improved constitutive model for different moduli, large deformation finite element formulations are developed and combined with the arc-length method, finite element iterative program for rods with different moduli is established to obtain buckling critical loads; Thirdly, material mechanical properties tests of graphite, which is the raw material of graphene, are performed to measure the tensile and compressive elastic moduli, moreover, buckling tests are also conducted to investigate the buckling behavior of this kind of graphite rod. By comparing the calculation results of the energy method and finite element method with those of laboratory tests, the analytical model and finite element numerical model are demonstrated to be accurate and reliable. The results show that it may lead to unsafe results if the classic theory was still adopted to determine the buckling loads of those rods composed of a material having different moduli. The proposed models could provide a novel approach for further investigation of non-linear mechanical behavior for other structures with different moduli.

• Steel and Composite Structures
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• 제44권2호
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Smart Structures and Systems
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• 제29권1호
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Structural Monitoring and Maintenance
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• 제10권3호
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• pp.207-220
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• 2023

The safety of bridges are critical in our transportation infrastructure. Bridge design and analysis require complex structural analysis procedures to ensure their safety and stability. One common method is to calculate the maximum moment in the girders to determine the appropriate bridge section. Girder distribution factors (GDFs) provide a simpler approach for performing this analysis. A GDF is a ratio between the response of a single girder and the total response of all girders in the bridge. This paper explores the significance of GDFs in bridge analysis and design, including their importance in the evaluation of existing bridges. We utilized Bridge Weigh-in-motion (B-WIM) measurements of five simple supported girder bridge in Indonesia to develop a simple GDF provisions for the Indonesia's bridge design code. The B-WIM measurements enable us to know each girder strain as a response due to vehicle loading as the vehicle passes the bridge. The calculated GDF obtained from the B-WIM measurements were compared with the code-specified GDF and the American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) bridge design specification. Our study found that the code specified GDF was adequate or conservative compared to the GDF obtained from the B-WIM measurements. The proposed GDF equation correlates well with the AASHTO LRFD bridge design specification. Developing appropriate provisions for GDFs in Indonesian bridge design codes can provides a practical solution for designing girder bridges in Indonesia, ensuring safety while allowing for easier calculations and assessments based on B-WIM measurements.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.226-234
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• 2024

The frequent handover problem and playing ping-pong effects in 5G (5th Generation) ultra-dense networking cannot be effectively resolved by the conventional handover decision methods, which rely on the handover thresholds and measurement reports. For instance, millimetre-wave LANs, broadband remote association techniques, and 5G/6G organizations are instances of group of people yet to come frameworks that request greater security, lower idleness, and dependable principles and correspondence limit. One of the critical parts of 5G and 6G innovation is believed to be successful blockage the board. With further developed help quality, it empowers administrator to run many systems administration recreations on a solitary association. To guarantee load adjusting, forestall network cut disappointment, and give substitute cuts in case of blockage or cut frustration, a modern pursuing choices framework to deal with showing up network information is require. Our goal is to balance the strain on BSs while optimizing the value of the information that is transferred from satellites to BSs. Nevertheless, due to their irregular flight characteristic, some satellites frequently cannot establish a connection with Base Stations (BSs), which further complicates the joint satellite-BS connection and channel allocation. SF redistribution techniques based on Deep Reinforcement Learning (DRL) have been devised, taking into account the randomness of the data received by the terminal. In order to predict the best capacity improvements in the wireless instruments of 5G and 6G IoT networks, a hybrid algorithm for deep learning is being used in this study. To control the level of congestion within a 5G/6G network, the suggested approach is put into effect to a training set. With 0.933 accuracy and 0.067 miss rate, the suggested method produced encouraging results.

• 지능정보연구
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• 제18권4호
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• pp.43-57
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• 2012

제조업에 있어서 판매 후 서비스 건수와 내용 등은 향후 서비스 제공을 위한 자원배분의 효율성 증진과 서비스 품질 향상을 위해서도 매우 중요한 정보이다. 따라서 기업들은 향후 발생하는 판매 후 서비스에 대해 정확히 예측하고 그에 따라 적절히 대처하는 능력을 확보할 필요성이 제조업을 중심으로 증가하고 있다. 그러나 실제로 이들 기업들이 활용하고 있는 서비스 수요예측 방법들은 전통적인 통계적인 예측기법이거나, 시뮬레이션을 기반한 기법들이다. 예를 들면, 전통적인 통계적인 예측기법으로는 회귀분석(regression analysis)의 경우, 다양한 제품모델에 대한 판매 후 서비스 발생 패턴이 선형적인 관계가 매우 적음에도 불구하고 선형으로 가정하여 추정한다는 점과 적정한 회귀식을 가정하여야 되며, 이러한 가정이 실제 경영환경에서는 매우 어렵다는 점 등이 기존의 예측기법들의 한계점으로 지적되고 있다. 본 연구에서는 디지털 TV 모델을 생산 판매 하는 A사의 사례연구를 통하여 최근 인공지능연구에서 각광을 받고 있는 사례기반추론(case-based reasoning; CBR) 기법을 활용한 서비스 수요예측 프레임워크를 제안하고자 한다. 또한, 사례기반추론에서 핵심적인 역할 중 하나인 유사 사례추출 방법에 있어서 가장 일반적인 nearest-neighbor 방법 이외의 유사 사례추출 방법을 제안하고자 한다. 특히, 본 연구에서 제안하는 유사 사례추출 방법은 인공신경망(artificial neural network)을 활용한 자기조직화지도(Self-Organizing Maps : SOM) 군집화 기법을 활용한 유사 사례추출 방식으로 이를 활용한 서비스 수요예측 프레임워크에 구현하고, 실제 기업의 판매 후 서비스 데이터를 활용하여 본 연구에서 제안하는 서비스 수요 예측 프레임워크의 유효성을 실증적으로 검증하고자 한다.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.345-353
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• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.

• 한국전산구조공학회논문집
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• 제34권3호
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• pp.137-149
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• 2021

장교는 공공성이 매우 높은 사회기반시설물로 운용 중 안전성 확보가 필수적이며, 붕괴 또는 파손 시 신속한 대처가 필요하다. 사장교의 붕괴 또는 파손을 야기시킬 수 있는 원인은 크게 자연재난과 사회재난으로 분류할 수 있다. 이 중 사회재난에 속하는 충돌사고는 차량이 교량 하부구조인 교각에 충돌하는 사고, 항공기의 결함으로 인한 추락사고 등이 있을 것이며, 해상교량의 경우 주탑 하단에서의 선박 충돌사고가 있을 것이다. 본 연구에서는 수치해석적 접근법을 기반으로 항공기 충돌에 대한 사장교의 구조거동을 평가하는 절차를 제안하고, 충돌해석을 수행하여 절차의 타당성을 보였다. 제안된 절차에는 1) 적절한 항공기 충돌 시나리오 설정, 2) 사장교의 복잡한 거동 메커니즘을 고려한 구조 모델링, 3) 충돌해석을 통한 구조거동 평가가 포함된다. 해석 결과, 본 연구에서 설정한 시나리오는 대상 교량에 큰 영향을 미치지 못하는 것으로 나타났지만, 향후 다양한 시나리오를 통한 충돌해석을 수행한다면 교량에 심각한 손상을 일으키는 하중 위치 및 임계 하중 수준을 결정할 수 있을 것으로 판단한다. 본 연구에서 수행한 충돌해석 절차를 바탕으로 사장교에서 발생하는 항공기 충돌에 대한 간접적인 평가가 가능할 것으로 기대된다.

• International Journal of Railway
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• 제2권3호
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• pp.124-126
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• 2009

Energy savings can be achieved with optimum energy consumptions, brake energy regeneration, efficient energy storage (onboard, line side), and primarily with light weight vehicles. Over the last few years, the rolling stock industry has experienced a marked increase in eco-awareness and needs for lower life cycle energy consumption costs. For rolling stock vehicle designers and engineers, weight has always been a critical design parameter. It is often specified directly or indirectly as contractual requirements. These requirements are usually expressed in terms of specified axle load limits, braking deceleration levels and/or demands for optimum energy consumptions. The contractual requirements for lower weights are becoming increasingly more stringent. Light weight vehicles with optimized strength to weight ratios are achievable through proven design processes. The primary driving processes consist of: $\bullet$ material selection to best contribute to the intended functionality and performance $\bullet$ design and design optimization to secure the intended functionality and performance $\bullet$ weight control processes to deliver the intended functionality and performance Aluminium has become the material of choice for modern light weight bodyshells. Steel sub-structures and in particular high strength steels are also used where high strength - high elongation characteristics out way the use of aluminium. With the improved characteristics and responses of composites against tire and smoke, small and large composite materials made components are also found in greater quantities in today's railway vehicles. Full scale hybrid composite rolling stock vehicles are being developed and tested. While an "overdesigned" bodyshell may be deemed as acceptable from a structural point of view, it can, in reality, be a weight saving missed opportunity. The conventional pass/fail structural criteria and existing passenger payload definitions promote conservative designs but they do not necessarily imply optimum lightweight designs. The weight to strength design optimization should be a fundamental design driving factor rather than a feeble post design activity. It should be more than a belated attempt to mitigate against contractual weight penalties. The weight control process must be rigorous, responsible, with achievable goals and above all must be integral to the design process. It should not be a mere tabulation of weights for the sole-purpose of predicting the axle loads and wheel balances compliance. The present paper explores and discusses the topics quoted above with a view to strengthen the recommendations and needs for the weight optimization by design approach as a pro-active design activity for the rolling stock industry at large.