Uncertainties

This paper presents a novel analytical formulation of an element removal algorithm based on dynamic equilibrium and the resulting transient change in system kinematics, by applying imposed accelerations instead of external forces at a node where an element was once connected. The algorithm is implemented into an open-source finite element code, numerically tested using a benchmark structural system with simplified element removal criteria, and able to capture the effect of uncertainty in member capacity. Realistic element removal criteria are introduced for mode-dependent gravity load collapse of seismically deficient and retrofitted reinforced concrete (RC) columns and unreinforced masonry (URM) infill walls. Two applications are conducted using structural systems of RC frames with URM infill walls. The first is a probabilistic study of a one-story model subjected to an ensemble of 14 ground motion recordings from similar neighboring sites during an earthquake event. The study produces empirical probability curves for partial and complete collapse conditioned on different hazard levels, and concludes that the intra-event variability is a major source of uncertainty affecting the outcome of progressive collapse simulations. The second application is a deterministic sensitivity study of progressive collapse response in a five-story structural model to uncertainty in live load, stiffness, damping, and seismic hazard level, subjected to one ground motion record. The analysis identifies the time at incipient collapse as an adequate sensitivity measure, and the uncertainty in ground motion intensity as the most important, followed by the stiffness of the URM infill wall. Copyright © 2009 John Wiley & Sons, Ltd.

Environmental time series are often affected by the “presence” of missing data, but when dealing statistically with data, the need to fill in the gaps estimating the missing values must be considered. At present, a large number of statistical techniques are available to achieve this objective; they range from very simple methods, such as using the sample mean, to very sophisticated ones, such as multiple imputation. A brand new methodology for missing data estimation is proposed, which tries to merge the obvious advantages of the simplest techniques (e.g. their vocation to be easily implemented) with the strength of the newest techniques. The proposed method consists in the application of two consecutive stages: once it has been ascertained that a specific monitoring station is affected by missing data, the “most similar” monitoring stations are identified among neighbouring stations on the basis of a suitable similarity coefficient; in the second stage, a regressive method is applied in order to estimate the missing data. In this paper, four different regressive methods are applied and compared, in order to determine which is the most reliable for filling in the gaps, using rainfall data series measured in the Candelaro River Basin located in South Italy.

Creating and managing protected areas is critical to ensure the persistence of species but dynamic threats like land-use change and climate change may reduce the effectiveness of protected areas planned under a static approach. Here we defined spatial priorities for the conservation of non-flying mammals inhabiting the Cerrado Biodiversity Hotspot, Brazil, that overcome the likely impacts of land-use and climate change to this imperiled fauna. We used cutting-edge methods of species distribution models combining thousands of model projections to generate a comprehensive ensemble of forecasts that shows the likely impacts of climate change in mammal distribution. We also generate a future land-use model that indicates how the region would be impacted by habitat loss in the future. We then used our models to propose priority sites for mammal conservation minimizing species climate-forced dispersal distance as well as the mean uncertainty associated to species distribution models and climate models. At the same time, our proposal maximizes complementary species representation across the existing network of protected areas. Including land-use changes and model uncertainties in the planning process changed significantly the spatial distribution of priority sites in the region. While the inclusion of land-use models altered the spatial location of priority sites at the regional scale, the effects of climate change tended to operate at the local scale. Our solutions already include possible dispersal corridors linking current and future priority sites for mammal conservation, as well as a formal risk analysis based on planning uncertainties. We hope to provide decision makers with conservation portfolios that could be negotiated at the decision level.► Land-use and climate changes challenge the way spatial conservation planning is done. ► Species distribution models are useful for such plans, but carry uncertainty. ► We included species range shifts and land-use changes in mammal conservation planning. ► Our spatial plans where improved by the inclusion of dynamics threats and uncertainty. ► We offer a methodological way to consider these threats in spatial planning.

Los campesinos no pueden sobrevivir exclusivamente del cultivo de café y deben complementar sus recursos con otras entradas económicas, sea la venta de productos secundarios, artesanías, del “jornaleo” o subsidios del gobierno. El sombrío es una práctica tradicional que ahorra recursos, demanda menos tecnología e implica menos riesgos de inversión. El cafetal con sombrío es relativamente más seguro y perdurable, por lo tanto garantiza un cultivo sostenible a largo plazo, con mayor estabilidad ambiental, económica y social.

El sombrío como estrategia proporciona seguridad alimentaria y social en las regiones cafeteras al proveer simultáneamente alimentos y beneficios económicos
extras. En algunas zonas de Colombia, los productos secundarios del sombrío juegan un papel determinante en la economía local, ya que se destinan en un importante porcentaje al autoconsumo, enriqueciendo la dieta alimenticia familiar.

Adicionalmente, su valor comercial puede llegar a superar los ingresos provenientes de la cosecha cafetera. La presente investigación se propone un acercamiento al
reconocimiento de los valores ambientales y económicos del sombrío desde las percepciones de los caficultores del Sur de Colombia (Departamentos de Huila y Cauca) y los niveles de importancia de los factores que inciden en sus decisiones
para el manejo de riesgos ecológicos y las incertidumbres económicas. Este trabajo introduce un esbozo histórico del sombrío en Colombia y una discusión sobre la consideración de la definición del sombrío.

El uso del sombrío ilustra la capacidad de adaptación e innovación de los productores locales versus una caficultura tecnificada y a plena exposición solar, estando en juego una caficultura tradicional sustentable pero subvalorada en el tiempo y espacio, donde los productores presentan múltiples estrategias económicas y ecológicas para disminuir los riesgos e incertidumbres y garantizar su supervivencia y seguridad alimentaria.

This paper presents a novel analytical formulation of an element removal algorithm based on dynamic equilibrium and the resulting transient change in system kinematics, by applying imposed accelerations instead of external forces at a node where an element was once connected. The algorithm is implemented into an open-source finite element code, numerically tested using a benchmark structural system with simplified element removal criteria, and able to capture the effect of uncertainty in member capacity. Realistic element removal criteria are introduced for mode-dependent gravity load collapse of seismically deficient and retrofitted reinforced concrete (RC) columns and unreinforced masonry (URM) infill walls. Two applications are conducted using structural systems of RC frames with URM infill walls. The first is a probabilistic study of a one-story model subjected to an ensemble of 14 ground motion recordings from similar neighboring sites during an earthquake event. The study produces empirical probability curves for partial and complete collapse conditioned on different hazard levels, and concludes that the intra-event variability is a major source of uncertainty affecting the outcome of progressive collapse simulations. The second application is a deterministic sensitivity study of progressive collapse response in a five-story structural model to uncertainty in live load, stiffness, damping, and seismic hazard level, subjected to one ground motion record. The analysis identifies the time at incipient collapse as an adequate sensitivity measure, and the uncertainty in ground motion intensity as the most important, followed by the stiffness of the URM infill wall. Copyright © 2009 John Wiley & Sons, Ltd.

Many organizations are increasingly relying on design simulation rather than expensive and time-consuming prototype testing for product evaluation. However, uncertainties in analytical and computational methods need to be understood in order to improve confidence in their use, and models need to be validated. This paper presents a case study of a MacPherson strut automotive suspension analysis, and evaluates the uncertainties in the modelling of this complex dynamic problem using a simplified analytical model and a complex computational model. In both cases, variability in design variables is characterized using probabilistic design methods. As a first step, the model variables are described by assumed datasets, which are collated from several sources such as tolerances specified in drawings, expert opinion, published data, etc. Measurement of the properties of the suspension system components is then performed (spring stiffness, damping coeffcient, etc.), and the statistical parame...

Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial condi- tions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formi- dable challenge. Recent enhancements in climate forecasting