• Advances in nano research
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• v.7 no.6
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• pp.443-457
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• 2019

In this investigation, dynamic and bending behaviors of isolated protein microtubules are analyzed. Microtubules (MTs) can be considered as bio-composite structures that are elements of the cytoskeleton in eukaryotic cells and posses considerable roles in cellular activities. They have higher mechanical characteristics such as superior flexibility and stiffness. In the modeling purpose of microtubules according to a hollow beam element, a novel single variable sinusoidal beam model is proposed with the conjunction of modified strain gradient theory. The advantage of this model is found in its new displacement field involving only one unknown as the Euler-Bernoulli beam theory, which is even less than the Timoshenko beam theory. The equations of motion are constructed by considering Hamilton's principle. The obtained results are validated by comparing them with those given based on higher shear deformation beam theory containing a higher number of variables. A parametric investigation is established to examine the impacts of shear deformation, length scale coefficient, aspect ratio and shear modulus ratio on dynamic and bending behaviors of microtubules. It is remarked that when length scale coefficients are almost identical of the outer diameter of MTs, microstructure-dependent behavior becomes more important.

• Wind and Structures
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• v.29 no.5
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• pp.339-359
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• 2019

Pre-stressed concrete poles are among the supporting systems used to support transmission lines. It is essential to protect transmission line systems from harsh environmental attacks such as downburst wind events. Typically, these poles are designed to resist synoptic wind loading as current codes do not address high wind events in the form of downbursts. In the current study, the behavior of guyed pre-stressed concrete Transmission lines is studied under downburst loads. To the best of the authors' knowledge, this study is the first investigation to assess the behaviour of guyed pre-stressed concrete poles under downburst events. Due to the localized nature of those events, identifying the critical locations and parameters leading to peak forces on the poles is a challenging task. To overcome this challenge, an in-house built numerical model is developed incorporating the following: (1) a three-dimensional downburst wind field previously developed and validated using computational fluid dynamics simulations; (2) a computationally efficient analytical technique previously developed and validated to predict the non-linear behaviour of the conductors including the effects of the pretension force, sagging, insulator's stiffness and the non-uniform distribution of wind loads, and (3) a non-linear finite element model utilized to simulate the structural behaviour of the guyed pre-stressed concrete pole considering material nonlinearity. A parametric study is conducted by varying the downbursts locations relative to the guyed pole while considering three different span values. The results of this parametric study are utilized to identify critical downburst configurations leading to peak straining actions on the pole and the guys. This is followed by comparing the obtained critical load cases to new load cases proposed to ASCE-74 loading committee. A non-linear failure analysis is then conducted for the three considered guyed pre-stressed concrete transmission line systems to determine the downburst jet velocity at which the pole systems fail.

• Steel and Composite Structures
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• v.31 no.4
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• pp.373-385
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• 2019

This paper presents a useful in-plane structural analysis of low-rise blind-bolted composite frames with semi-rigid joints. Analytical models were used to predict the moment-rotation relationship of the composite beam-to-column flush endplate joints that produced accurate and reliable results. The comparisons of the analytical model with test results in terms of the moment-rotation response verified the robustness and reliability of the model. Abaqus software was adopted to conduct frame analysis considering the material and geometrical non-linearities. The flexural behaviour of the composite frames was studied by applying the lateral loads incorporating wind and earthquake actions according to the Australian standards. A wide variety of frames with a varied number of bays and storeys was analysed to determine the bending moment envelopes under different load combinations. The design models were finalized that met the strength and serviceability limit state criteria. The results from the frame analysis suggest that among lateral loads, wind loads are more critical in Australia as compared to the earthquake loads. However, gravity loads alone govern the design as maximum sagging and hogging moments in the frames are produced as a result of the load combination with dead and live loads alone. This study provides a preliminary analysis and general understanding of the behaviour of low rise, semi-continuous frames subjected to lateral load characteristics of wind and earthquake conditions in Australia that can be applied in engineering practice.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.5
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• pp.1-8
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• 2020

This study presents a shear strength estimation model, in which the shear failure of a reinforced concrete (RC) member is assumed to be governed by the flexure-shear mechanism. Two shear demand curves and corresponding potential capacity curves for cracked tension and uncracked compression zones are derived, for which the bond mechanism developed between reinforcing bars and surrounding concrete is considered in flexural analysis. The shear crack concentration factor is also addressed to consider the so-called size effect induced in large RC members. In addition,unlike exising methods, a new formulation was addressed to consider the interaction between the shear contributions of concrete and stirrup. To verify the proposed method, an extensive shear database was established, and it appeared that the proposed method can capture the shear strengths of the collected test specimens regardless of their material properties, geometrical features, presence of stirrups, and bond characteristics.

• Advances in nano research
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• v.10 no.3
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• pp.253-261
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• 2021

Polydimethylsiloxane (PDMS) is one of the most widely adopted silicon-based organic polymeric elastomers. Elastomeric nanostructures are normally required to accomplish an explicit mechanical role and correspondingly their mechanical properties are crucial to affect device and material performance. Despite its wide application, the mechanical properties of PDMS are yet fully understood. In particular, the time dependent mechanical response of PDMS has not been fully elucidated. Here, utilizing state-of-the-art PeakForce Quantitative Nanomechanical Mapping (PFQNM) together with Force Volume (FV) and Fast Force Volume (FFV), the elastic moduli of PDMS samples were assessed in a time-dependent fashion. Specifically, the acquisition frequency was discretely changed four orders of magnitude from 0.1 Hz up to 2 kHz. Careful calibrations were done. Force data were fitted with a linearized DMT contact mechanics model considering surface adhesion force. Increased Young's modulus was discovered with increasing acquisition frequency. It was measured 878 ± 274 kPa at 0.1 Hz and increased to 4586 ± 758 kPa at 2 kHz. The robust local probing of mechanical measurement as well as unprecedented high-resolution topography imaging open new avenues for quantitative nanomechanical mapping of soft polymers, and can be extended to soft biological systems.

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.441-461
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• 2021

Reinforced concrete (RC) columns are crucial in building structures and they are of higher vulnerability to terrorist threat than any other structural elements. Thus it is of great interest and necessity to achieve a comprehensive understanding of the possible responses of RC columns when exposed to high intensive blast loads. The primary objective of this study is to derive analytical formulas to assess vulnerability of RC columns using an advanced numerical modelling approach. This investigation is necessary as the effect of blast loads would be minimal to the RC structure if the explosive charge is located at the safe standoff distance from the main columns in the building and therefore minimizes the chance of disastrous collapse of the RC columns. In the current research, finite element model is developed for RC columns using LS-DYNA program that includes a comprehensive discussion of the material models, element formulation, boundary condition and loading methods. Numerical model is validated to aid in the study of RC column testing against the explosion field test results. Residual capacity of RC column is selected as damage criteria. Intensive investigations using Arbitrary Lagrangian Eulerian (ALE) methodology are then implemented to evaluate the influence of scaled distance, column dimension, concrete and steel reinforcement properties and axial load index on the vulnerability of RC columns. The generated empirical formulae can be used by the designers to predict a damage degree of new column design when consider explosive loads. With an extensive knowledge on the vulnerability assessment of RC structures under blast explosion, advancement to the convention design of structural elements can be achieved to improve the column survivability, while reducing the lethality of explosive attack and in turn providing a safer environment for the public.

• Composites Research
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• v.36 no.5
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• pp.329-334
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• 2023

The classical multiscale finite element (FE² ) method involves iterative calculations of micro-boundary value problems for representative volume elements at every integration point in macro scale, making it a computationally time and data storage space. To overcome this, we developed the data-driven multiscale analysis method based on the mean-field homogenization (MFH). Data-driven computational mechanics (DDCM) analysis is a model-free approach that directly utilizes strain-stress datasets. For performing multiscale analysis, we efficiently construct a strain-stress database for the microstructure of composite materials using mean-field homogenization and conduct data-driven computational mechanics simulations based on this database. In this paper, we apply the developed multiscale analysis framework to an example, confirming the results of data-driven computational mechanics simulations considering the microstructure of a hyperelastic composite material. Therefore, the application of data-driven computational mechanics approach in multiscale analysis can be applied to various materials and structures, opening up new possibilities for multiscale analysis research and applications.

• Steel and Composite Structures
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• v.52 no.3
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• pp.273-291
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• 2024

This paper presents a three-dimensional displacement-based formulation to investigate the free vibration of functionally graded nanoplates resting on a Winkler-Pasternak foundation based on the nonlocal elasticity theory. The material properties of the FG nanoplate are considered to vary continuously through the thickness of the nanoplate according to the power-law distribution model. A general three-dimensional displacement field is considered for the plate, which takes into account the out-of-plane strains of the plate as well as the in-plane strains. Unlike the shear deformation theories, in the present formulation, no predetermined form for the distribution of displacements and transverse strains is considered. The equations of motion for functionally graded nanoplate are derived based on Hamilton's principle. The solution is obtained for simply-supported nanoplate, and the predicted results for natural frequencies are compared with the predictions of shear deformation theories which are available in the literature. The predictions of the present theory are discussed in detail to investigate the effects of power-law index, length-to-thickness ratio, mode numbers and the elastic foundation on the dynamic behavior of the functionally graded nanoplate. The present study presents a three-dimensional solution that is able to determine more accurate results in predicting of the natural frequencies of flexural and thickness modes of nanoplates. The effects of parameters that play a key role in the analysis and mechanical design of functionally graded nanoplates are investigated.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Applied Chemistry for Engineering
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• v.28 no.6
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• pp.705-713
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• 2017

A novel material, $Bi_2WO_6-GO-TiO_2$ composite, was successfully synthesized using a facile hydrothermal method. During the hydrothermal reaction, the loading of $Bi_2WO_6$ and $TiO_2$ nanoparticles onto graphene sheets was achieved. The obtained $Bi_2WO_{6-GO-TiO2}$ composite photo-catalysts were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray (EDX) analysis, transmission electron microscopy (TEM), Raman spectroscopy, ultraviolet-visible diffuse reflectance spectroscopy (UV-vis-DRS), and X-ray photoelectron spectroscopy (XPS). The $Bi_2WO_6$ nanoparticle showed an irregular dark-square block nanoplate shape, while $TiO_2$ nanoparticles covered the surface of the graphene sheets with a quantum dot size. The degradation of rhodamine B (RhB), methylene blue trihydrate (MB), and reactive black B (RBB) dyes in an aqueous solution with different initial amount of catalysts was observed by UV spectrophotometry after measuring the decrease in the concentration. As a result, the $Bi_2WO_6-GO-TiO_2$ composite showed good decolorization activity with MB solution under visible light. The $Bi_2WO_6-GO-TiO_2$ composite is expected to become a new potential material for decolorization activity. Photocatalytic reactions with different photocatalysts were explained by the Langmuir-Hinshelwood model and a band theory.