Probabilistic seismic damage assessment of reinforced concrete buildings considering directionality effects

Most of buildings and structures are usually projected according to two main axes. However, the geographical position of these buildings varies randomly. Such random distributions of the azimuthal positions of structures, in most of the cities, generally, are not accounted for when assessing their seismic risk; certainly, the direction of the seismic loads is another highly random variable. Moreover, an additional important source of uncertainty is related to the structural response, mainly due to the random character of the mechanical properties. There is a consensus that uncertainties must be considered for adequately assessing the seismic risk of structures, but these directionality effects have not been deeply explored so far. In this article, the influence of the high uncertainty involved in these input variables on the expected seismic damage is analysed. Thus, an actual earthquake, which affected the southern part of Spain, is studied. Notably, damages on a group of affected buildings, located close to the epicentre, are analysed and discussed in detail. The results show that the influence of the random azimuthal position of structures is an important source of uncertainty and that it should be taken into account when estimating the expected seismic risk in urban areas.     

The special‐linear update: An application of differential manifold theory to the update of isochoric plasticity flow rules

The evolution of plastic deformations in metals, governed by incompressible flow rules, has been traditionally solved using the exponential mapping. However, the accurate calculation of the exponential mapping and its tangents may result in computationally demanding schemes in some cases, while common low‐order approximations may lead to poor behavior of the constitutive update because of violation of the incompressibility condition. Here, we introduce the special‐linear (SL) update for isochoric plasticity, a flow‐rule integration scheme based on differential manifolds concepts. The proposed update exactly enforces the plastic incompressibility condition while being first‐order accurate and consistent with the flow rule, thus bearing all the desirable properties of the now standard exponential mapping update. In contrast to the exponential‐mapping update, we demonstrate that the SL update can drastically reduce the computing time, reaching one order of magnitude speed‐ups in the calculation of the update tangents. We demonstrate the applicability of the update by way of simulation of single‐crystal plasticity uniaxial loading tests. We anticipate that the SL update will open the way to efficient constitutive updates for the solution of complex multiscale material models, thus making it a very promising tool for large‐scale simulations. Copyright © 2013 John Wiley & Sons, Ltd.     

Relationship between vegetation of the levee neo‐ecosystems and environmental heterogeneity in the Lower Delta of the Paraná River,Argentina

The relationship between environmental heterogeneity and the vegetation of the levee neo‐ecosystems in the Delta of the Paraná River was studied. These habitat types were considered plant communities of recent origin related to local productive activities. Vegetation coverage was evaluated in 97 plots of three different environmental units (A, B and C), using classification analysis, indirect and direct ordinations. The differential vegetation in different environmental units could be related to a greater fluvial influence of the Paraná River on unit A and a greater tidal influence of the de la Plata River on units B and C. The Lower Delta hydrological regimes only affect a few of edaphic variables particularly pH, organic matter percentage and clay content. To understand the spatial pattern of neo‐ecosystems vegetation, it is also necessary to consider the invasion of alien species that has been taking place for over 50 years.     

Identification of phases and precipitates by AFM in duplex stainless steel unions

ABSTRACT

Atomic Force Microscopy (AFM), a characterization technique that generates topographic images of surfaces at very high resolutions, works by recording the surface relief details of the material with a cantilever which moves over the sample, while a piezoelectric sensor monitors changes in height. This technique is useful for identifying phases, particles and precipitates on a nanoscale and is therefore proposed for identifying them in the fusion zone of welded components and for early identification of faults due to cracking. This is a technique that does not require conductive samples, or special metallographic preparation of specimens as is the case with electron microscopy. The material studied is a duplex 2205 stainless steel, welded by means of gas tungsten arc welding (GTAW). The welded material was subjected to stress tests. The test specimens were characterized using optical microscopy, scanning electron microscopy, atomic force microscopy and nanoindentation tests. The results focus on the identification of the specific magnetic, topographic and hardness characteristics of each of the phases, particles and areas present in the base metal, the fusion zone and the zone deformed by the fracture.     

Natural and human forcing in recent geomorphic change; case studies in the Rio de la Plata basin

An analysis of geomorphic system's response to change in human and natural drivers in some areas within the Río de la Plata basin is presented. The aim is to determine whether an acceleration of geomorphic processes has taken place in recent years and, if so, to what extent it is due to natural (climate) or human (land-use) drivers. Study areas of different size, socio-economic and geomorphic conditions have been selected: the Río de la Plata estuary and three sub-basins within its watershed. Sediment cores were extracted and dated (²¹⁰Pb) to determine sedimentation rates since the end of the 19th century. Rates were compared with time series on rainfall as well as human drivers such as population, GDP, livestock load, crop area, energy consumption or cement consumption, all of them related to human capacity to disturb land surface. Data on river discharge were also gathered. Results obtained indicate that sedimentation rates during the last century have remained essentially constant in a remote Andean basin, whereas they show important increases in the other two, particularly one located by the São Paulo metropolitan area. Rates in the estuary are somewhere in between. It appears that there is an intensification of denudation/sedimentation processes within the basin.Rainfall remained stable or varied very slightly during the period analysed and does not seem to explain increases of sedimentation rates observed. Human drivers, particularly those more directly related to capacity to disturb land surface (GDP, energy or cement consumption) show variations that suggest human forcing is a more likely explanation for the observed change in geomorphic processes. It appears that a marked increase in denudation, of a “technological” nature, is taking place in this basin and leading to an acceleration of sediment supply. This is coherent with similar increases observed in other regions.     

An optimization method for learning statistical classifiers in structural reliability

Monte Carlo simulation is a general and robust method for structural reliability analysis, affected by the serious efficiency problem consisting in the need of computing the limit state function a very large number of times. In order to reduce this computational effort the use of several kinds of solver surrogates has been proposed in the recent past. Proposals include the Response Surface Method (RSM), Neural Networks (NN), Support Vector Machines (SVM) and several other methods developed in the burgeoning field of Statistical Learning (SL). Many of these techniques can be employed either for function approximation (regression approach) or for pattern recognition (classification approach). This paper concerns the use of these devices for discriminating samples into safe and failure classes using the classification approach, because it constitutes the core of Monte Carlo simulation as applied to reliability analysis as such. Due to the flexibility of most SL methods, a critical step in their use is the generation of the learning population, as it affects the generalization capacity of the surrogate. To this end it is first demonstrated that the optimal population from the information viewpoint lies around in the vicinity of the limit state function. Next, an optimization method assuring a small as well as highly informative learning population is proposed on this basis. It consists in generating a small initial quasi-random population using Sobol sequence for triggering a Particle Swarm Optimization (PSO) performed over an iteration-dependent cost function defined in terms of the limit state function. The method is evaluated using SVM classifiers, but it can be readily applied also to other statistical classification techniques because the distinctive feature of the SVM, i.e. the margin band, is not actively used in the algorithm. The results show that the method yields results for the probability of failure that are in very close agreement with Monte Carlo simulation performed on the original limit state function and requiring a small number of learning samples.     

Use of image analysis to determine fat and connective tissue in salmon muscle

 An attempt was made to measure connective tissue and fat in salmon fillets using image analysis. Farmed salmon fillets were digitized by video camera and transferred to an image analysis program. A Laplace transformation was performed, and the image was segmented into different scales of grey. Particle analysis was applied to the binary pictures to calculate the percentage of white stripes in the total surface of the fillet, which were related to the percentage of connective tissue (CT) and fat. Although low correlation coefficients were obtained, it could be assumed that the video image analysis method is sensitive enough to detect great variations in fat content, which could prove useful in industrial applications. In contrast, CT was found to have less influence on the visual appraisal of the fillet surface. Received: 17 July 1998 / Revised version: 20 October 1998     

Nice Perspective Projections

A polyhedral object in three-dimensional space is often well represented by a set of points and line segments that act as its features. By a nice perspective projection of an object we mean a projection that gives an image in which the features of the object, relevant for some task, are visible without ambiguity. In this paper we consider the problem of computing a variety of nice perspective projections of three-dimensional objects such as simple polygonal chains, wire-frame drawings of graphs, and geometric rooted trees. These problems arise in areas such as computer vision, computer graphics, graph drawing, knot theory, and computational geometry.     

Adaptive Polarized Waveform Design for Target Tracking Based on Sequential Bayesian Inference

In this paper, we develop an adaptive waveform design method for target tracking under a framework of sequential Bayesian inference. We employ polarization diversity to improve the tracking accuracy of a target in the presence of clutter. We use an array of electromagnetic (EM) vector sensors to fully exploit the polarization information of the reflected signal. We apply a sequential Monte Carlo method to track the target parameters, including target position, velocity, and scattering coefficients. This method has the advantage of being able to handle nonlinear and non-Gaussian state and measurement models. The measurements are the output of the sensor array; hence, the information about both the target and its environment is incorporated in the tracking process. We design a new criterion for selecting the optimal waveform one-step ahead based on a recursion of the posterior Cramer-Rao bound. We also derive an algorithm using Monte Carlo integration to compute this criterion and a suboptimal method that reduces the computation cost. Numerical examples demonstrate both the performance of the proposed tracking method and the advantage of the adaptive waveform design scheme.