• Earthquakes and Structures
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• v.17 no.5
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• pp.447-462
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• 2019

This present paper concerned with the analytic modelling for vibration of the functionally graded (FG) plates resting on non-variable and variable two parameter elastic foundation, based on two-dimensional elasticity using higher shear deformation theory. Our present theory has four unknown, which mean that have less than other higher order and lower theory, and we denote do not require the factor of correction like the first shear deformation theory. The indeterminate integral are introduced in the fields of displacement, it is allowed to reduce the number from five unknown to only four variables. The elastic foundations are assumed a classical model of Winkler-Pasternak with uniform distribution stiffness of the Winkler coefficient (kw), or it is with variables distribution coefficient (kw). The variable's stiffness of elastic foundation is supposed linear, parabolic and trigonometry along the length of functionally plate. The properties of the FG plates vary according to the thickness, following a simple distribution of the power law in terms of volume fractions of the constituents of the material. The equations of motions for natural frequency of the functionally graded plates resting on variables elastic foundation are derived using Hamilton principal. The government equations are resolved, with respect boundary condition for simply supported FG plate, employing Navier series solution. The extensive validation with other works found in the literature and our results are present in this work to demonstrate the efficient and accuracy of this analytic model to predict free vibration of FG plates, with and without the effect of variables elastic foundations.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.148-148
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• 2018

Land subsidence caused by excessive groundwater pumping is a serious hydro-geological hazard. The spatial variability in land use, unbalanced groundwater extraction and aquifer characteristics are the key factors which make the problem more difficult to monitor using conventional methods. This study uses the European Space Agency (ESA) Sentinel satellites to investigate and monitor land subsidence varying with different land covers and groundwater use in the arid Quetta valley, Pakistan. The Persistent Scattering Differential Interferometry of Synthetic Aperture Radar (PS-DInSAR) method was used to develop 28 subsidence interferograms of the study area for the period between 16 Oct 2014 and 06 Oct 2016 using ESA's Sentinel-1 SAR data. The uncertainty of DInSAR result is first minimized by removing the dynamic effect caused by atmospheric factors and then filtered using the radar Amplitude Dispersion Index (ADI) to select only the stable pixels. Finally the subsidence maps were generated by spatially interpolating the land subsidence at the stable pixels, the comparison of DInSAR subsidence with GPS readings showed an R 2 of 0.94 and mean absolute error of $5.7{\pm}4.1mm$. The subsidence maps were also analysed for the effect of aquifer type and 4 land covers which were derived from Sentienl-2 multispectral images. The analysis show that during the two year period, the study area experienced highly non-linear land subsidence ranging from 10 to 280 mm. The subsidence at different land covers was significantly different from each other except between the urban and barren land. The barren land and seasonally cultivated area show minor to moderate subsidence while the orchard and urban area with high groundwater extraction rate showed excessive amount of land subsidence. Moreover, the land subsidence and groundwater drawdown was found to be linearly proportional to each other.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.259-272
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• 2022

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

• Journal of Integrative Natural Science
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• v.15 no.3
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• pp.99-110
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• 2022

Solar photovoltaic (PV) system shows a non-linear current (I) -voltage (V) characteristics, which depends on the surrounding environment factors, such as irradiance, temperature, and the wind. Solar PV system, with current (I) - voltage (V) and power (P) - Voltage (V) characteristics, specifies a unique operating point at where the possible maximum power point (MPP) is delivered. At the MPP, the PV array operates at maximum power efficiency. In order to continuously harvest maximum power at any point of time from solar PV modules, a good MPPT algorithms need to be employed. Currently, due to its simplicity and easy implementation, Perturb and Observe (P&O) algorithms are the most commonly used MPPT control method in the PV systems but it has a drawback at suddenly varying environment situations, due to constant step size. In this paper, to overcome the difficulties of the fast changing environment and suddenly changing the power of PV array due to constant step size in the P&O algorithm, least mean Square (LMS) methods is proposed together with P&O MPPT algorithm which is superior to traditional P&O MPPT. PV output power is predicted using LMS method to improve the tracking speed and deduce the possibility of misjudgment of increasing and decreasing the PV output. Simulation results shows that the proposed MPPT technique can track the MPP accurately as well as its dynamic response is very fast in response to the change of environmental parameters in comparison with the conventional P&O MPPT algorithm, and improves system performance.

• Steel and Composite Structures
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• v.46 no.4
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• pp.451-469
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• 2023

The use of precast hollow-core slabs (PCHCS) in civil construction has been increasing due to the speed of execution and reduction in the weight of flooring systems. However, in the literature there are no studies that present a finite element model (FEM) to predict the load-slip relationship behavior of pushout tests, considering headed stud shear connector and PCHCS placed at the upper flange of the downstand steel profile. Thus, the present paper aims to develop a FEM, which is based on tests to fill this gap. For this task, geometrical non-linear analyses are carried out in the ABAQUS software. The FEM is calibrated by sensitivity analyses, considering different types of analysis, the friction coefficient at the steel-concrete interface, as well as the constitutive model of the headed stud shear connector. Subsequently, a parametric study is performed to assess the influence of the number of connector lines, type of filling and height of the PCHCS. The results are compared with analytical models that predict the headed stud resistance. In total, 158 finite element models are processed. It was concluded that the dynamic implicit analysis (quasi-static) showed better convergence of the equilibrium trajectory when compared to the static analysis, such as arc-length method. The friction coefficient value of 0.5 was indicated to predict the load-slip relationship behavior of all models investigated. The headed stud shear connector rupture was verified for the constitutive model capable of representing the fracture in the stress-strain relationship. Regarding the number of connector lines, there was an average increase of 108% in the resistance of the structure for models with two lines of connectors compared to the use of only one. The type of filling of the hollow core slab that presented the best results was the partial filling. Finally, the greater the height of the PCHCS, the greater the resistance of the headed stud.

• 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.

• Korean Journal of Biological Psychiatry
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• v.4 no.1
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• pp.67-73
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• 1997

The changes of electroencephalogram(EEG) in patients with dementia are most commonly studied by analyzing power or magnitude in certain traditionally defined frequency bands. However because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to chaos theory, irregular signals of EEG can also result from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the correlation dimension. The authors have analyzed EEG epochs from three patients with dementia of Alzheimer type and three matched control subjects. The multichannel correlation dimension is calculated from EEG epochs consisting of 15 channels with 16,384 data points per channel. The results showed that patients with dementia of Alzheimer type had significantly lower correlation dimension than non-demented controls on 12 channels. Topographic analysis showed that the correlation dimensions were significantly lower in patients with Alzheimer's disease on frontal, temporal, central, and occipital head regions. These results show that brains of patients with dementia of Alzheimer type have a decreased complexity of electrophysiological behavior. We conclude that the nonlinear analysis such as calculating correlation dimension can be a promising tool for detecting relative changes in the complexity of brain dynamics.

• Journal of Advanced Marine Engineering and Technology
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• v.41 no.3
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• pp.202-208
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• 2017

The recently developed marine engines for propulsion of ships have higher torsional exciting force than previous engines to improve the propulsion efficiency and to reduce specific fuel oil consumption. As a result, a viscous damper or viscous-spring damper is installed in front of marine engine to control the torsional vibration. In the case of viscous damper, it is supposed that there is no elastic connection in the silicon oil, which is filled between the damper housing and inertia ring. However, In reality, the silicon oil with high viscosity possesses torsional stiffness and has non-linear dynamic characteristics according to the operating temperature and frequency of the viscous damper. In this study, the damping characteristics of a viscous damper used to control the torsional vibration of the shafting system have been reviewed and the characteristics of torsional vibration of the shafting system equipped with a corresponding viscous damper have been examined. In addition, it is examined how to interpret the theoretically optimal dynamic characteristics of a viscous damper for this purpose, and the optimum design for the propulsion shafting system has been suggested considering the operating temperature and aging. when the torsional vibration of the shafting system is controlled by a viscous damper filled with highly viscous silicon oil.

• Journal of the Korean Geotechnical Society
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• v.26 no.7
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• pp.49-58
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• 2010

This paper presents experimental results of a series of 1-g shaking table model tests performed on end-bearing single piles and pile groups to investigate the effect of particle size on the dynamic behavior of soil-pile systems. Two soil-pile models were tested twice: first using Jumoonjin sand, and second using Australian Fine sand. In the case of single-pile models, the lateral displacement was almost within 1% of pile diameter which corresponds to the elastic range of the pile. The back-calculated p-y curves show that the subgrade reaction of the Jumoonjin-sand-model ground was larger than that of the Australian Fine-sand-model ground at the same displacement. This phenomenon means that the stress-strain behavior of Jumoonjin sand was initially stiffer than that of Australian Fine sand. This difference was also confirmed by resonant column tests and compression triaxial tests. And the single pile p-y backbone curves of the Australian fine sand were constructed and compared with those of the Jumoonjin sand. As a result, the stiffness of the p-y backbone curves of Jumunjin sand was larger than those of Australian fine sand. Therefore, using the same p-y curves regardless of particle size can lead to inaccurate results when evaluating dynamic behavior of soil-pile system. In the case of the group-pile models, the lateral displacement was much larger than the elastic range of pile movement at the same test conditions in the single-pile models. The back-calculated p-y curves in the case of group pile models were very similar in both sands because the stiffness difference between the Jumoonjin-sand-model ground and the Australian Fine-sand-model ground was not significantly large at a large strain level, where both sands showed non-linear behavior. According to a series of single pile and group pile test results, the evaluation group pile effect using the p-multiplier can lead to inaccurate results on dynamic behavior of soil-pile system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2A
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• pp.89-96
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• 2011

The damping ratio as an index of bridge vibration could be considered as one of the important dynamic characteristics of a suspension bridge. But estimating of damping ratio on an existing suspension bridge under ambient vibration condition could be a laborious task. Moreover, it is not simple to directly distinguish aerodynamic damping and friction damping from apparent damping. According to previous studies, the aerodynamic damping properties can be linearly affected by wind speed level, and apparent damping ratio can be affected by amplitude of vibration. Therefore, in this article, the relationships among damping ratio, wind speed level and amplitude of acceleration were studied for separating extract aerodynamic damping and friction damping from apparent damping. Damping ratios on Sorok Bridge, a suspension bridge which is a located in Go-Heung, Korea, were estimated by two different methods as using Hilbert transform and extended Kalman filter which were well known as effective estimation methods for non-linear state. It was possible to distinguish aerodynamic damping and friction damping from apparent damping using averaged normal components of wind speed, RMQ values of acceleration, and estimated damping ratios from wind-induced vibration responses and vehicle loading responses.