• Geomechanics and Engineering
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• v.27 no.5
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• pp.465-479
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• 2021

One of the important causes of building and infrastructure failure, such as bridges on pile foundations, is the placement of the piles in liquefiable soil that can become unstable under seismic loads. Therefore, the overarching aim of this study is to investigate the seismic behavior of a soil-pile system in liquefiable soil using three-dimensional numerical FEM analysis, including soil-pile interaction. Effective parameters on concrete pile response, involving the pile diameter, pile length, soil type, and base acceleration, were considered in the framework of finite element non-linear dynamic analysis. The constitutive model of soil was considered as elasto-plastic kinematic-isotropic hardening. First, the finite element model was verified by comparing the variations on the pile response with the measured data from the centrifuge tests, and there was a strong agreement between the numerical and experimental results. Totally 64 non-linear time-history analyses were conducted, and the responses were investigated in terms of the lateral displacement of the pile, the effect of the base acceleration in the pile behavior, the bending moment distribution in the pile body, and the pore pressure. The numerical analysis results demonstrated that the relationship between the pile lateral displacement and the maximum base acceleration is non-linear. Furthermore, increasing the pile diameter results in an increase in the passive pressure of the soil. Also, piles with small and big diameters are subjected to yielding under bending and shear states, respectively. It is concluded that an effective stress-based ground response analysis should be conducted when there is a liquefaction condition in order to determine the maximum bending moment and shear force generated within the pile.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.4
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• pp.402-410
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• 2022

The present study introduces a calibration method of the CSC model implemented in the LS-DYNA program for considering the material properties of ultra-high performance concrete(UHPC). Based on previous experimental studies, various parameters, which constitute three shear failure surfaces, pressure-volumetric strain curve, fracture energy, dynamic increase factor(DIF), and so on, are modified. Then, the proposed calibration method is verified by comparing the numerical result with the experimental data through the single element analysis. In addition, based on the established finite element models, the applicability of the calibrated CSC model is examined for UHPC structures subjected to impact and blast loadings.

• Smart Structures and Systems
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• v.29 no.2
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• pp.267-278
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• 2022

Pre-stressed concrete circular spun piles are widely used in various infrastructure projects around the world and offer an economical deep foundation system with consistent and superior quality compared to cast in-situ and other concrete piles. Conventional methods for measuring the lateral response of piles have been limited to conventional instrumentation, such as electrical based gauges and pressure transducers. The problem with existing technology is that the sensors are not able to assist in recording the lateral stiffness changes of the pile which varies along the length depending on the distribution of the flexural moments and appearance of tensile cracks. This paper describes a full-scale bending test of a 1-m diameter spun pile of 30 m long and instrumented using advanced fibre optic distributed sensor, known as Brillouin Optical Time Domain Analysis (BOTDA). Optical fibre sensors were embedded inside the concrete during the manufacturing stage and attached on the concrete surface in order to measure the pile's full-length flexural behaviour under the prescribed serviceability and ultimate limit state. The relationship between moments-deflections and bending moments-curvatures are examined with respect to the lateral forces. Tensile cracks were measured and compared with the peak strains observed from BOTDA data which corroborated very well. By analysing the moment-curvature response of the pile, the structure can be represented by two bending stiffness parameters, namely the pre-yield (EI) and post-yield (EI_cr), where the cracks reduce the stiffness property by 89%. The pile deflection profile can be attained from optical fibre data through closed-form solutions, which generally matched with the displacements recorded by Linear Voltage Displacement Transducers (LVDTs).

• Wind and Structures
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• v.34 no.2
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• pp.185-197
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• 2022

This study aimed to analyze the wind-induced mechanical energy (WME) of a proposed super high-rise and long-span transmission tower-line system (SHLTTS), which, in 2021, is the tallest tower-line system with the longest span. Anew index - the WME, accounting for the wind-induced vibration behavior of the whole system rather than the local part, was first proposed. The occurrence of the maximum WME for a transmission tower, with or without conductors, under synoptic winds, was analyzed, and the corresponding formulae were derived based on stochastic vibration theory. Some calculation data, such as the drag coefficient, dynamic parameters, windshielding areas, mass, calculation point coordinates, mode shape and influence function, derived from wind tunnel testing on reducedscale models and finite element software were used in calculating the maximum WME of the transmission tower under three cases. Then, the influence of conductors, wind speed, gradient wind height and wind yaw angle on WME components and the energy transfer relationship between substructures (transmission tower and conductor) were analyzed. The study showed that the presence of conductors increases the WME of transmission towers and changes the proportion of the mean component (MC), background component (BC) and resonant component (RC) for WME; The RC of WME is more susceptible to the wind speed change. Affected by the gradient wind height, the WME components decrease. With the RC decreasing the fastest and the MC decreasing the slowest; The WME reaches the its maximum value at the wind yaw angle of 30°. Due to the influence of three factors, namely: the long span of the conductors, the gradient wind height and the complex geometrical profile, it is important that the tower-line coupling effect, the potential for fatigue damage and the most unfavorable wind yaw angle should be given particular attention in the wind-resistant design of SHLTTSs

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.11
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• pp.92-99
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• 2021

Agricultural tractors drive and operate both off-road and on-road. Tire-road interaction significantly affects the tractive performance of a tractor, which is difficult to predict numerically. Many empirical models have been developed to predict the tractive performance of tractors using the cone index, which can be measured through simple tests. However, a magic formula model that can determine the tractive performance without a cone index can be used instead of traditional empirical models as the cone index cannot be measured on asphalt roads. The aim of this study was to predict the tractive performance of a tractor using the magic formula tire model. The traction force of the tires on an asphalt road was measured using an agricultural tractor. The dynamic wheel load was calculated to derive the coefficients of the traction-slip curve using the measured static wheel load and drawbar pull of the tractor. Curve fitting was performed to fit the experimental data using the magic formula. The parameters of the magic formula tire model were well identified, and the model successfully determined the coefficient of traction of the tractor.

• Wind and Structures
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• v.36 no.4
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• pp.221-236
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• 2023

The lateral component of turbulence and the vortices shed in the wake of a structure result in introducing dynamic wind load in the acrosswind direction and the resulting level of motion is typically larger than the corresponding alongwind motion for a dynamically sensitive structure. The underlying source mechanisms of the acrosswind load may be classified into motion-induced, buffeting, and Strouhal components. This study proposes a frequency domain framework to decompose the overall load into these components based on output-only measurements from wind tunnel experiments or full-scale measurements. First, the total acrosswind load is identified based on measured acceleration response by solving the inverse problem using the Kalman filter technique. The decomposition of the combined load is then performed by modeling each load component in terms of a Bayesian filtering scheme. More specifically, the decomposition and the estimation of the model parameters are accomplished using the unscented Kalman filter in the frequency domain. An aeroelastic wind tunnel experiment involving a tall circular cylinder was carried out for the validation of the proposed framework. The contribution of each load component to the acrosswind response is assessed by re-analyzing the system with the decomposed components. Through comparison of the measured and the re-analyzed response, it is demonstrated that the proposed framework effectively decomposes the total acrosswind load into components and sheds light on the overall underlying mechanism of the acrosswind load and attendant structural response. The delineation of these load components and their subsequent modeling and control may become increasingly important as tall slender buildings of the prismatic cross-section that are highly sensitive to the acrosswind load effects are increasingly being built in major metropolises.

• Steel and Composite Structures
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• v.45 no.3
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• pp.409-423
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• 2022

Nowadays, there is a high demand for great structural implementation and multifunctionality with excellent mechanical properties. The porous structures reinforced by graphene platelets (GPLs) having valuable properties, such as heat resistance, lightweight, and excellent energy absorption, have been considerably used in different engineering implementations. However, stiffness of porous structures reduces significantly, due to the internal cavities, by adding GPLs into porous medium, effective mechanical properties of the porous structure considerably enhance. This paper is relating to vibration analysis of fluidconveying cantilever porous graphene platelet reinforced (GPLR) pipe with fractional viscoelastic model resting on foundations. A dynamical model of cantilever porous GPLR pipes conveying fluid and resting on a foundation is proposed, and the vibration, natural frequencies and primary resonant of such a 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 the fractional viscoelastic model is used to govern the construction relation of nanocomposite pipe. Three different porosity distributions through the pipe thickness are introduced. The harmonic concentrated force is also applied to the pipe and the excitation frequency is close to the first natural frequency. The governing equation for transverse motions 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.13-21
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• 2023

This paper proposes a method of automatically generating a model through a machine learning technique by setting a hypothetical model in the form of a gray box or black box with unknown parameters, when the big data of the actual system is given. We implements the proposed framework and conducts experiments to find an appropriate model among various hypothesis models and compares the cost and fitness of them. As a result we find that the proposed framework works well with continuous systems that could be modeled with ordinary differential equation. This technique is expected to be used well for the purpose of automatically updating the consistency of the digital twin model or predicting the output for new inputs using recently generated big data.