• Steel and Composite Structures
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• v.50 no.2
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• pp.201-216
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• 2024

This paper is motivated by the lack of studies relating to vibration and nonlinear resonance of fluid-conveying cantilever porous GPLR pipes with fractional viscoelastic model resting on nonlinear foundations. A dynamical model of cantilever porous Graphene Platelet Reinforced (GPLR) pipes conveying fluid and resting on nonlinear foundation is proposed, and the vibration, natural frequencies and primary resonant of such system are explored. The pipe body is considered to be composed of GPLR viscoelastic polymeric pipe with porosity in which Halpin-Tsai scheme in conjunction with fractional viscoelastic model is used to govern the construction relation of the nanocomposite pipe. Three different porosity distributions through the pipe thickness are introduced. The harmonic concentrated force is also applied on pipe and excitation frequency is close to the first natural frequency. The governing equation for transverse motion of the pipe is derived by the Hamilton principle and then discretized by the Galerkin procedure. In order to obtain the frequency-response equation, the differential equation is solved with the assumption of small displacement, damping coefficient, and excitation amplitude by the multiple scale method. A parametric sensitivity analysis is carried out to reveal the influence of different parameters, such as nanocomposite pipe properties, fluid velocity and nonlinear viscoelastic foundation coefficients, on the primary resonance and linear natural frequency. Results indicate that the GPLs weight fraction porosity coefficient, fractional derivative order and the retardation time have substantial influences on the dynamic response of the system.

• Structural Monitoring and Maintenance
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• v.10 no.4
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• pp.315-337
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• 2023

During the design phase, it is crucial to determine the interface stresses between the reinforcing plate and the concrete base in order to predict plate end separation failures. In this work, a simple theoretical study of interface shear stresses in beams reinforced with P-FGM and E-FGM plates subjected to an arbitrarily positioned point load, or two symmetrical point loads, was presented using the linear elastic theory. The presence of pores in the reinforcing plate distributed in several forms was also taken into account. For this purpose, we analyze the effects of porosity and its distribution shape on the interracial normal and shear stresses of an FGM beam reinforced with an FRP plate under different types of load. Comparisons of the proposed model with existing analytical solutions in the literature confirm the feasibility and accuracy of this new approach. The influence of different parameters on the interfacial behavior of reinforced concrete beams reinforced with functionally graded porous plates is further examined in this parametric study using the proposed model. From the results obtained in this study, we can say that interface stress is significantly affected by several factors, including the pores present in the reinforcing plate and their distribution shape. Additionally, we can conclude from this study that reinforcement systems with composite plates are very effective in improving the flexural response of reinforced RC beams.

• Structural Engineering and Mechanics
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• v.89 no.6
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• pp.557-570
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• 2024

Due to interfacial ageing, chemical action and interfacial damage, the interface debonding may appear in the interfaces of composite laminates. Particularly, the laminates display a side-dependent effect at small scale. In this work, a three-dimensional (3D) and anisotropic thick nanoplate model is proposed to investigate the effects of imperfect interface and nonlocal parameter on the bending deformation, vibrational response and buckling stability of one-dimensional (1D) hexagonal quasicrystal (QC) layered nanoplates. By combining the linear spring model with the transferring matrix method, exact solutions of phonon and phason displacements, phonon and phason stresses of bending deformation, the natural frequencies of vibration and the critical buckling loads of 1D hexagonal QC layered nanoplates are derived with imperfect interfaces and nonlocal effects. Numerical examples are illustrated to demonstrate the effects of the imperfect interface parameter, aspect ratio, thickness, nonlocal parameter, and stacking sequence on the bending deformation, the vibrational response and the critical buckling load of 1D hexagonal QC layered nanoplate. The results indicate that both the interface debonding and nonlocal effect can reduce the stiffness and stability of layered nanoplates. Increasing thickness of QC coatings can enhance the stability of sandwich nanoplates with the perfect interfaces, while it can reduce first and then enhance the stability of sandwich nanoplates with the imperfect interfaces. The biaxial compression easily results in an instability of the QC layered nanoplates compared to uniaxial compression. QC material is suitable for surface layers in layered structures. The mechanical behavior of QC layered nanoplates can be optimized by imposing imperfect interfaces and controlling the stacking sequence artificially. The present solutions are helpful for the various numerical methods, thin nanoplate theories and the optimal design of QC nano-composites in engineering practice with interfacial debonding.

• Structural Engineering and Mechanics
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• v.89 no.6
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• pp.589-599
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• 2024

This study presents the usability of the high-rate single-frequency Precise Point Positioning (SF-PPP) technique based on 20 Hz Global Positioning Systems (GPS)-only observations in detecting dynamic motions. SF-PPP solutions were obtained from post-mission and real-time GNSS corrections. These include the International GNSS Service (IGS)-Final, IGS real-time (RT), real-time MADOCA (Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis), and real-time products from the Australian/New Zealand satellite-based augmentation systems (SBAS, known as SouthPAN). SF-PPP results were compared with LVDT (Linear Variable Differential Transformer) sensor and single-frequency relative positioning (SF-RP) solutions. The findings show that the SF-PPP technique successfully detects the harmonic motions, and the real-time products-based PPP solutions were as accurate as the final post-mission products. In the frequency domain, all GNSS-based methods evaluated in this contribution correctly detect the dominant frequency of short-term harmonic oscillations, while the differences in the amplitude values corresponding to the peak frequency do not exceed 1.1 mm. However, evaluations in the time domain show that SF-PPP needs high-pass filtering to detect accurate displacement since SF-PPP solutions include trends and low-frequency fluctuations, mainly due to atmospheric effects. Findings obtained in the time domain indicate that final, real-time, and MADOCA-based PPP results capture short-term dynamic behaviors with an accuracy ranging from 3.4 mm to 8.5 mm, and SBAS-based PPP solutions have several times higher RMSE values compared to other methods. However, after high-pass filtering, the accuracies obtained from PPP methods decreased to a few mm. The outcomes demonstrate the potential of the high-rate SF-PPP method to reliably monitor structural and earthquake-induced ground motions and vibration frequencies of structures.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.471-476
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• 2024

With the spread of various smart devices and computing devices, big data generation is occurring widely. Machine learning is an algorithm that performs reasoning by learning data patterns. Among the various machine learning algorithms, the algorithm that attracts attention is deep learning based on neural networks. Deep learning is achieving rapid performance improvement with the release of various applications. Recently, among deep learning algorithms, attempts to analyze data using graph structures are increasing. In this study, we present a graph generation method for transferring to a deep learning network. This paper proposes a method of generalizing node properties and edge weights in the graph generation process and converting them into a structure for deep learning input by presenting a matricization We present a method of applying a linear transformation matrix that can preserve attribute and weight information in the graph generation process. Finally, we present a deep learning input structure of a general graph and present an approach for performance analysis.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.511-525
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• 2024

Micropiled raft has been used to support the existing and new structures or to provide the seismic reinforcement of foundation systems. Recently, research on micropile or micropiled raft has been actively conducted as the usage of micropile has increased, and the reinforcement effect of pile for the raft, the pile installation methods, and methods for calculating the bearing capacity of micropiled raft have been proposed. In addition, existing research results show that the behavior of this foundation system is different depending on the pile conditions and can be greatly influenced by the characteristics of the upper or lower ground depending on the conditions of pile. In other words, considering that the micropile is a friction pile, it can be predicted that the reinforcing effect of micropile for the raft and the bearing capacity of micropiled raft may depend on the cohesion of upper soil layer depending on the pile conditions. However, existing studies have limitations in that they were conducted without taking this into account. However, existing studies have limitations as they have been conducted without considering these characteristics. Accordingly, this study investigated the reinforcing effect of micropile and the bearing characteristics of micropiled raft by varying the cohesion of upper soil layer and the stiffness of pile which affect the behavior of micropiled raft. In this results, the reinforcing effect of micropile on the raft also increased as the cohesion of soil layer increased, but the reinforcing effect of pile was more effective in ground conditions with decreased the cohesion. In addition, the relationship between the axial stiffness of micropile and the bearing capacity of micropiled raft was found to be a logarithmic linear relationship. It was found that the reinforcing effect of micropile can increase the bearing capacity of raft by 1.33~ 3.72 times depending on the cohesion of soil layer and the rigidity of pile.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.453-465
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• 2024

This paper presents a logarithmic shear deformation theory to study the thermal buckling response of power-law FG one-dimensional structures in thermal conditions with different boundary conditions. It is assumed that the functionally graded material and thermal properties are supposed to vary smoothly according to a contentious function across the vertical direction of the beams. A P-FG type function is employed to describe the volume fraction of material and thermal properties of the graded (1D) beam. The Ritz model is employed to solve the thermal buckling problems in immovable boundary conditions. The outcomes of the stability analysis of FG beams with temperature-dependent and independent properties are presented. The effects of the thermal loading are considered with three forms of rising: nonlinear, linear and uniform. Numerical results are obtained employing the present logarithmic theory and are verified by comparisons with the other models to check the accuracy of the developed theory. A parametric study was conducted to investigate the effects of various parameters on the critical thermal stability of P-FG beams. These parameters included support type, temperature fields, material distributions, side-to-thickness ratios, and temperature dependency.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.4
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• pp.133-140
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• 2008

The design method of the reinforced concrete structures is converting from the current limit state design method to the reliability based design method and active studies have been done in the US, Europe, and Japan etc. Performance based design method is considering lots of uncertainty of current design provision rationally and make sure that structure have a reliable reliability and safety. The main area of these studies is to secure the non-linear analysis technology with high reliability. The data for reinforced concrete columns tested by many researchers are used to verify the applicability of the nonlinear finite element analysis program (RCAHEST, Reinforced Concrete Analysis in Higher Evaluation System Technology). A comparison is made between analysis and test, calculated safety factor based on reliability theories to applies to analysis result.

• Steel and Composite Structures
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• v.52 no.4
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• pp.391-403
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• 2024

The flexural behavior of composite sandwich wall panels with different thicknesses, numbers of holes, and hole forms, and arrangement form of longitudinal steel bar (uniform type and concealed-beam type) are investigated. A total of twelve composite sandwich wall panels are prepared, utilizing modified polystyrene particles mixed with foam concrete for the flexural performance test. The failure pattern of the composite sandwich wall panels is influenced by the extruded polystyrene panel (XPS) panel thickness and the reinforcement ratio in combination, resulting in both flexural and shear failure modes. Increasing the XPS panel thickness causes the specimens to transition from flexural failure to shear failure. An increase in the reinforcement ratio leads to the transition from flexural failure to shear failure. The hole form on the XPS panel and the steel bar arrangement form affect the loading behavior of the specimens. Plum-arrangement hole form specimens exhibit lower steel bar strain and deflection compared to linear-arrangement hole form specimens. Additionally, specimens with concealed beam-type steel bar display lower steel bar strain and deflection than uniform-type steel bar specimens. However, the hole form and steel bar arrangement form have a limited impact on the ultimate load. Theoretical formulas for cracking load are provided for both fully composite and non-composite states. When compared to the experimental values, it is observed that the cracking load of the specimens with XPS panels closely matches the calculations for the non-composite state. An accurate prediction model for the ultimate load of fully composite wall panels is developed. These findings offer valuable insights into the behavior of composite sandwich wall panels and provide a basis for predicting their performance under various design factors and conditions.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.167-183
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• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.