• Structural Engineering and Mechanics
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• v.54 no.5
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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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• v.44 no.2
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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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• v.29 no.1
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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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• v.10 no.3
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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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• v.24 no.1
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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.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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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.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.3
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• pp.137-149
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• 2021

Cable-stayed bridges are infrastructure facilities of a highly public nature; therefore, it is essential to ensure operational safety and prompt response in the event of a collapse or damage caused by natural and social disasters. Among social disasters, impact accidents can occur in bridges when a vehicle collides with a pier or when crashes occur due to aircraft defects. In the case of offshore bridges, ship collisions will occur at the bottom of the pylon. In this research, a procedure to evaluate the structural behavior of a cable-stayed bridge for aircraft impact is suggested based on a numerical analysis approach, and the feasibility of the procedure is demonstrated by performing an example assessment. The suggested procedure includes 1) setting up suitable aircraft impact hazard scenarios, 2) structural modeling considering the complex behavior mechanisms of cable-stayed bridges, and 3) structural behavior evaluation of cable-stayed bridges using numerical impact simulation. It was observed that the scenario set in this study did not significantly affect the target bridge. However, if impact analysis is performed through various scenarios in the future, the load position and critical load level to cause serious damage to the bridge could be identified. The scenario-based assessment process employed in this study is expected to facilitate the evaluation of bridge structures under aircraft impact in both existing bridges and future designs.

• International Journal of Railway
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• v.2 no.3
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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.