• Structural Engineering and Mechanics
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• 제81권1호
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.

• 대기
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• 제25권1호
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• pp.179-183
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• 2015

Even though state-of-the-art general circulation models is improved step by step, the seasonal predictability of the East Asian summer monsoon still remains poor. In contrast, the seasonal predictability of western North Pacific and Indian monsoon region using dynamic models is relatively high. This study builds canonical correlation analysis model for seasonal prediction using wind fields over western North Pacific and Indian Ocean from the Global Seasonal Forecasting System version 5 (GloSea5), and then assesses the predictability of so-called hybrid model. In addition, we suggest improvement method for forecast skill by introducing the lagged ensemble technique.

• Advances in Traditional Medicine
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• 제8권3호
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• pp.215-221
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• 2008

In this present study Indian traditional Ayurvedic herbal formulation Amritaristo has been studied to assess the general pharmacological effect on mice. The drug showed no significant activity on the neuropharmacological test models experimented. The increased pentobarbital sleeping time was considered related with hepatic metabolism of pentobarbital. The formulation exhibited a non-significant reduction of gastrointestinal motility, and devoid of any acute diuretic activity. The tested drug revealed antidiarrhoeal activity on castor oil-induced model, whereas on magnesium sulphate-induced model no effect was observed.

• Ocean Systems Engineering
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• 제8권2호
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• pp.201-221
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• 2018

In this paper a comprehensive study for the structural control of Jacket platform with Magneto-Rheological (MR) damper is presented. The control is implemented as a closed loop feedback of the applied voltage in the MR Damper using fuzzy logic. Nine cases of combinations with MR damper are presented to complete the work. The selection of the MR damper (RD 1005-3) is based on the operating parameters (i.e., the range of frequency and displacement). Bingham model is used to obtain the control forces. The damping co-efficient of the model is obtained using empirical relationship between the voltage in the MR damper and input velocity from the structural members. The force acting on the structure is obtained from Morison equation using P-M spectrum. The results show that the reliable control was obtained when there was a continuous connection of multiple MR dampers with the lower levels of the structure. Independent MR dampers at different levels provided control within a range, while the MR dampers placed at alternate positions gave very high control.

• Structural Engineering and Mechanics
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• 제60권5호
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• pp.829-849
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• 2016

In this paper, experimental as well as numerical analysis of Glass Fiber Reinforced Polymer (GFRP) laminated composite has been presented under ballistic impact with varying projectile nose shapes (conical, ogival and spherical) and incidence velocities. The experimental impact tests on GFRP composite plate reinforced with woven glass fiber ($0^{\circ}/90^{\circ}$)s are performed by using pneumatic gun. A three dimensional finite element model is developed in AUTODYN hydro code to validate the experimental results and to study the ballistic perforation characteristic of the target with different parametric variations. The influence of projectile nose shapes, plate thickness and incidence velocity on the variation of residual velocity, ballistic limit, contact force-time histories, energy absorption, damage pattern and damage area in the composite target have been studied. The material characterization of GFRP composite is carried out as required for the progressive damage analysis of composite. The numerical results from the present FE model in terms of residual velocity, absorbed energy, damage pattern and damage area are having close agreement with the results from the experimental impact tests.

• Earthquakes and Structures
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• 제5권3호
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• pp.359-378
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• 2013

The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.

• Geomechanics and Engineering
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• 제1권4호
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• pp.307-321
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• 2009

The paper employs a feed forward neural network with back-propagation algorithm for modeling time dependent swell in clays containing carbonate in the presence of sulfuric acid. The oedometer swell percent is estimated at a nominal surcharge pressure of 6.25 kPa to develop 612 data sets for modeling. The input parameters used in the network include time, sulfuric acid concentration, carbonate percentage, and liquid limit. Among the total data sets, 280 (46%) were assigned to training, 175 (29%) for testing and the remaining 157 data sets (25%) were relegated to cross validation. The network was programmed to process this information and predict the percent swell at any time, knowing the variable involved. The study demonstrates that it is possible to develop a general BPNN model that can predict time dependent swell with relatively high accuracy with observed data ($R^2$=0.9986). The obtained results are also compared with generated non-linear regression model.

• Advances in environmental research
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• 제3권2호
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• pp.117-129
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• 2014

Groundwater contamination is seeking a lot of attention due to constant degradation of water by landfills and waste lagoons. In many cases heterogeneous soil system is encountered and hence, a finite element model is developed to solve the advection-dispersion equation for layered soil system as FEM is a robust tool for modelling problems of heterogeneity and complex geometries. Recently developed Meshfree methods have advantage of eliminating the mesh and construct approximate solutions and are observed that they perform effectively as compared to conventional FEM. In the present study, both FEM and Meshfree method are used to simulate phenomenon of contaminant transport in one dimension. The results obtained are agreeing with the values in literature and hence the model is further used for predicting the transport of contaminants. Parametric study is done by changing the dispersion coefficient, average velocity, geochemical reactions, height of leachate and height of liner for obtaining suitability.

• Journal of Industrial and Engineering Chemistry
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• 제67권
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• pp.448-460
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• 2018

Copper nanoparticle-doped and graphitic carbon nanofibers-covered porous carbon beads were used as an efficient catalyst for treating synthetic phenolic water by catalytic wet air oxidation (CWAO) in a packed bed reactor over 10-30 bar and $180-230^{\circ}C$, with air and water flowing co-currently. A mathematical model based on reaction kinetics assuming degradation in both heterogeneous and homogeneous phases was developed to predict reduction in chemical oxygen demand (COD) under a continuous operation with recycle. The catalyst and process also showed complete COD reduction (>99%) without leaching of Cu against a high COD (~120,000 mg/L) containing industrial wastewater.

• Asia pacific journal of information systems
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• 제30권3호
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• pp.532-546
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• 2020

The current study examined the Indian young consumer online shopping self-efficacy in an integrated model. Authors tested the study model (antecedents and consequences of online shopping involvement) with the help of 225 sample data by using first order structural equation modeling. Online shopping enjoyment was found most important predictor of online shopping involvement followed by online accessibility. Further, the impact of online shopping involvement on online shopping self-efficacy was quite high in comparison to online technical self-efficacy. We strongly recommend that the marketers must use internet strategically in establishing dialogue between seller and online shoppers.