Effects of periodic fluctuations of micro‐polar boundary conditions on shear localizations in granular soil–structure interaction

In this work, the interface behavior between an infinite extended narrow granular layer and a rough surface of rigid body is investigated numerically, using finite element method in the updated Lagrangian (UL) frame. In this regard, the elasto‐plastic micro‐polar (Cosserat) continuum approach is employed to remove the limitations caused by strain‐softening of materials in the classical continuum. The mechanical properties of cohesionless granular soil are described with Lade's model enhanced by polar terms, including Cosserat rotations, curvatures, and couple stresses. Furthermore, the mean grain diameter as the internal length is incorporated into the constitutive relations accordingly. Here, the evolution and location of shear band, within the granular layer in contact with the rigid body, are mainly focused. In this regard, particular attention is paid to the effects of homogeneous distribution and periodic fluctuation of micro‐polar boundary conditions, prescribed along the interface. Correspondingly, the effects of pressure level, mean grain diameter, and stratified soil are also considered. The finite element results demonstrate that the location and evolution of shear band in the granular soil layer are strongly affected by the non‐uniform micro‐polar boundary conditions, prescribed along the interface. It is found that the shear band is located closer to the boundary with less restriction of grain rotations. Furthermore, the predicted thickness of shear band is larger for higher rotation resistance of soil grains along the interface, larger mean grain diameter, and higher vertical pressure. Regarding the stratified soil, comprising a thin layer with slightly different initial void ratio, the shear band moves towards the layer with initially higher void ratio. Copyright © 2011 John Wiley & Sons, Ltd.     

Wave dispersion in high‐rise buildings due to soil–structure interaction

Nonparametric techniques for estimation of wave dispersion in buildings by seismic interferometry are applied to a simple model of a soil–structure interaction (SSI) system with coupled horizontal and rocking response. The system consists of a viscously damped shear beam, representing a building, on a rigid foundation embedded in a half‐space. The analysis shows that (i) wave propagation through the system is dispersive. The dispersion is characterized by lower phase velocity (softening) in the band containing the fundamental system mode of vibration, and little change in the higher frequency bands, relative to the building shear wave velocity. This mirrors its well‐known effect on the frequencies of vibration, i.e. reduction for the fundamental mode and no significant change for the higher modes of vibration, in agreement with the duality of the wave and vibrational nature of structural response. Nevertheless, the phase velocity identified from broader band impulse response functions is very close to the superstructure shear wave velocity, as found by an earlier study of the same model. The analysis reveals that (ii) the reason for this apparent paradox is that the latter estimates are biased towards the higher values, representative of the higher frequencies in the band, where the response is less affected by SSI. It is also discussed that (iii) bending flexibility and soil flexibility produce similar effects on the phase velocities and frequencies of vibration of a building. Copyright © 2014 John Wiley & Sons, Ltd.     

Accounting for Parameter Uncertainty in Reservoir Uncertainty Assessment: The Conditional Finite-Domain Approach

An important aim of modern geostatistical modeling is to quantify uncertainty in geological systems. Geostatistical modeling requires many input parameters. The input univariate distribution or histogram is perhaps the most important. A new method for assessing uncertainty in the histogram, particularly uncertainty in the mean, is presented. This method, referred to as the conditional finite-domain (CFD) approach, accounts for the size of the domain and the local conditioning data. It is a stochastic approach based on a multivariate Gaussian distribution. The CFD approach is shown to be convergent, design independent, and parameterization invariant. The performance of the CFD approach is illustrated in a case study focusing on the impact of the number of data and the range of correlation on the limiting uncertainty in the parameters. The spatial bootstrap method and CFD approach are compared. As the number of data increases, uncertainty in the sample mean decreases in both the spatial bootstrap and the CFD. Contrary to spatial bootstrap, uncertainty in the sample mean in the CFD approach decreases as the range of correlation increases. This is a direct result of the conditioning data being more correlated to unsampled locations in the finite domain. The sensitivity of the limiting uncertainty relative to the variogram and the variable limits are also discussed.     

Failure risk assessment of interdependent infrastructures against earthquake,a Petri net approach: case study—power and water distribution networks

Metropolitan areas consist of complicated systems of interconnected infrastructures that are highly interdependent. Disruption of one infrastructure may induce disruption in other interconnected ones. The results from analysis of one infrastructure as an independent system are not realistic without considering the behavior of other interconnected infrastructures. Consequently, the study of the interdependencies among critical infrastructures is important for addressing the cascading effects of a failed infrastructure on the entire network to properly model its performance and help the disaster management team in decision making. In this study, the extended Petri net and Markov chain have been used to demonstrate the power and water infrastructure interdependency with a case study of one of the municipal districts of metropolitan Tehran, the capital of Iran. In this research, three cases have been assessed quantitatively: (1) the intra-dependency effects of different components in each network, (2) the interdependency effects between the considered critical infrastructures and (3) the behavior of the water network considering intra- and interdependency, when the power network fails. The analyses show that considering the mentioned interdependencies has a major influence on their performance simulations and assessment of their exact vulnerability. It is concluded that the failure probability of the water network that is dependent on the failed power network is 1.66 of the independent water network in the studied region. Eventually, the results of the research could be used in design, restoration and disaster management planning for safety assessment of critical infrastructures.     

Online Multi-step Ahead Prediction of Time-Varying Solar and Geomagnetic Activity Indices via Adaptive Neurofuzzy Modeling and Recursive Spectral Analysis

The time-varying Sun as the main source of space weather affects the Earth??s magnetosphere by emitting hot magnetized plasma in the form of solar wind into interplanetary space. Solar and geomagnetic activity indices and their chaotic characteristics vary abruptly during solar and geomagnetic storms. This variation depicts the difficulties in modeling and long-term prediction of solar and geomagnetic storms. On the other hand, the combination of neurofuzzy models and spectral analysis has been a subject of interest due to their many practical applications in modeling and predicting complex phenomena. However, these approaches should be trained by algorithms that need to be carried out by an offline data set, which influences their performance in online modeling and prediction of time-varying phenomena. This paper proposes an adaptive approach for multi-step ahead prediction of space weather indices by extending the regular singular spectrum analysis and locally linear neurofuzzy models to adaptive approaches. The combination of these recursive approaches fulfills requirements of long-term prediction of solar and geomagnetic activity indices. The results demonstrate the power of the proposed method in online prediction of space weather indices.