Examinando por Materia "Urban growth"

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    Development of structural debris flow fragility curves (debris flow buildings resistance) using momentum flux rate as a hazard parameter
    (Elsevier B.V., 2018-05-18) Prieto, Jorge Alonso; Journeay, Murray; Acevedo A.B.; Arbelaez, Juan; Ulmi, Malaika; Prieto, Jorge Alonso; Journeay, Murray; Acevedo A.B.; Arbelaez, Juan; Ulmi, Malaika; Universidad EAFIT. Departamento de Ingeniería de Producción; Materiales de Ingeniería
    Societal risks associated with debris flow hazards are significant and likely to escalate due to global population growth trends and the compounding effects of climate change. Quantitative risk assessment methods (QRA) provide a means of anticipating the likely impacts and consequences of settlement in areas susceptible to landslide activity and are increasingly being used to inform land use decisions that seek to increase disaster resilience through mitigation and/or adaptation. Current QRA methods for debris flow hazards are based primarily on empirical vulnerability functions that relate hazard intensity (depth, velocity, etc.) to expected levels of loss for a given asset of concern, i.e. most of current methods are dedicated to loss-intensity relations. Though grounded in observed cause-effect relationships, empirical vulnerability functions are not designed to predict the capacity of a building to withstand the physical impacts of a debris flow event, or the related uncertainties associated with modelling building performance as a function of variable debris flow parameters. This paper describes a methodology for developing functions that relate hazard intensity to probability of structural damage, i.e., fragility functions, rather than vulnerability functions, based on the combined hydrodynamic forces of a debris flow event (hazard level) and the inherent structural resistance of building typologies that are common in rural mountainous settings (building performance). Hazard level includes a hydrodynamic force variable (FDF), which accounts for the combined effects of debris flow depth and velocity, i.e. momentum flux (hv2), material density (?) and related flow characteristics including drag (Cd) and impact coefficient (Kd). Building performance is measured in terms of yield strength (Ay), ultimate lateral capacity (AU) and weight to breadth ratios (W/B) defined for a portfolio building types that are common in mountain settlements. Collectively, these model parameters are combined using probabilistic methods to produce building-specific fragility functions that describe the probability of reaching or exceeding successive thresholds of structural damage over a range of hazard intensity values, expressed in terms of momentum flux. Validation of the proposed fragility model is based on a comparison between model outputs and observed cause-effect relationships for recent debris flow events in South Korea and in Colombia. Debris flow impact momentum fluxes, capable of resulting in complete damage to unreinforced masonry buildings (URM) in those regions are estimated to be on the order of 24 m3/s2, consistent with field-based observations. Results of our study offer additional capabilities for assessing risks associated with urban growth and development in areas exposed to debris flow hazards. © 2018 Elsevier B.V.
  • No hay miniatura disponible
    Ítem
    Development of structural debris flow fragility curves (debris flow buildings resistance) using momentum flux rate as a hazard parameter
    (Elsevier B.V., 2018-05-18) Prieto, Jorge Alonso; Journeay, Murray; Acevedo A.B.; Arbelaez, Juan; Ulmi, Malaika; Mecánica Aplicada
    Societal risks associated with debris flow hazards are significant and likely to escalate due to global population growth trends and the compounding effects of climate change. Quantitative risk assessment methods (QRA) provide a means of anticipating the likely impacts and consequences of settlement in areas susceptible to landslide activity and are increasingly being used to inform land use decisions that seek to increase disaster resilience through mitigation and/or adaptation. Current QRA methods for debris flow hazards are based primarily on empirical vulnerability functions that relate hazard intensity (depth, velocity, etc.) to expected levels of loss for a given asset of concern, i.e. most of current methods are dedicated to loss-intensity relations. Though grounded in observed cause-effect relationships, empirical vulnerability functions are not designed to predict the capacity of a building to withstand the physical impacts of a debris flow event, or the related uncertainties associated with modelling building performance as a function of variable debris flow parameters. This paper describes a methodology for developing functions that relate hazard intensity to probability of structural damage, i.e., fragility functions, rather than vulnerability functions, based on the combined hydrodynamic forces of a debris flow event (hazard level) and the inherent structural resistance of building typologies that are common in rural mountainous settings (building performance). Hazard level includes a hydrodynamic force variable (FDF), which accounts for the combined effects of debris flow depth and velocity, i.e. momentum flux (hv2), material density (?) and related flow characteristics including drag (Cd) and impact coefficient (Kd). Building performance is measured in terms of yield strength (Ay), ultimate lateral capacity (AU) and weight to breadth ratios (W/B) defined for a portfolio building types that are common in mountain settlements. Collectively, these model parameters are combined using probabilistic methods to produce building-specific fragility functions that describe the probability of reaching or exceeding successive thresholds of structural damage over a range of hazard intensity values, expressed in terms of momentum flux. Validation of the proposed fragility model is based on a comparison between model outputs and observed cause-effect relationships for recent debris flow events in South Korea and in Colombia. Debris flow impact momentum fluxes, capable of resulting in complete damage to unreinforced masonry buildings (URM) in those regions are estimated to be on the order of 24 m3/s2, consistent with field-based observations. Results of our study offer additional capabilities for assessing risks associated with urban growth and development in areas exposed to debris flow hazards. © 2018 Elsevier B.V.
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    Leptospirosis risk around a potential source of infection
    (SPIE-INT SOC OPTICAL ENGINEERING, 2015-01-01) Erica, Loaiza-Echeverry; Doracelly, Hincapie-Palacio; Acosta Jesus, Ochoa; Giraldo Juan, Ospina; Erica, Loaiza-Echeverry; Doracelly, Hincapie-Palacio; Acosta Jesus, Ochoa; Giraldo Juan, Ospina; Universidad EAFIT. Departamento de Ciencias; Lógica y Computación
    Leptospirosis is a bacterial zoonosis with world distribution and multiform clinical spectrum in men and animals. The etiology of this disease is the pathogenic species of Leptospira, which cause diverse manifestations of the disease, from mild to serious, such as the Weil disease and the lung hemorrhagic syndrome with lethal proportions of 10% - 50%. This is an emerging problem of urban health due to the growth of marginal neighborhoods without basic sanitary conditions and an increased number of rodents. The presence of rodents and the probability of having contact with their urine determine the likelihood for humans to get infected. In this paper, we simulate the spatial distribution of risk infection of human leptospirosis according to the proximity to rodent burrows considered as potential source of infection. The Bessel function K0 with an r distance from the potential point source, and the scale parameter a in meters was used. Simulation inputs were published data of leptospirosis incidence rate (range of 5 to 79 x 10 000), and a distance of 100 to 5000 meters from the source of infection. We obtained an adequate adjustment between the function and the simulated data. The risk of infection increases with the proximity of the potential source. This estimation can become a guide to propose effective measures of control and prevention. © 2015 SPIE.
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    Maraña tropical : herramientas para la gestión integral territorial del ecosistema estratégico de manglar en bahía El Uno, Municipio de Turbo, Antioquia
    (Universidad EAFIT, 2024) Bedoya Viana , María Margarita; Cantillo Escobar, Ruth Isolina; López-Rodríguez, Sara Raquel
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    Spatiotemporal dynamics of urban growth in Latin American cities: An analysis using nighttime light imagery
    (ELSEVIER SCIENCE BV, 2019-01-01) Duque J.C.; Lozano-Gracia N.; Patino J.E.; Restrepo P.; Velasquez W.A.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)
    The impact of urban form on economic performance and quality of life has been widely recognized. Studies regarding urban form have focused on developed countries; only a small number of cities in developing countries have been studied. This paper utilizes nighttime light imagery and information regarding street networks, automatically retrieved from OpenStreetMap, to calculate a series of spatial metrics that capture different aspects of the urban form of 919 Latin American and Caribbean cities. We study the relationship between the urban form metrics and several factors that can correlate with urban form (topography, size, colony, and economic performance) and perform a spatiotemporal analysis of urban growth from 1996 to 2010. Among the results, we highlight the tendency of a group of cities to grow on steeper slopes and several worrying aspects, specifically urban growth in protected areas and a trend to sprawl-growing in certain Latin American and Caribbean cities. © 2019 Elsevier B.V.
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    Spatiotemporal Modeling of Urban Growth Using Machine Learning
    (MDPI AG, 2019-12-28) Duque, J.; Jorge E. Patino; Gomez, J.; Passos, S.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)
    This paper presents a general framework for modeling the growth of three important variables for cities: population distribution, binary urban footprint, and urban footprint in color. The framework models the population distribution as a spatiotemporal regression problem using machine learning, and it obtains the binary urban footprint from the population distribution through a binary classifier plus a temporal correction for existing urban regions. The framework estimates the urban footprint in color from its previous value, as well as from past and current values of the binary urban footprint using a semantic inpainting algorithm. By combining this framework with free data from the Landsat archive and the Global Human Settlement Layer framework, interested users can get approximate growth predictions of any city in the world. These predictions can be improved with the inclusion in the framework of additional spatially distributed input variables over time subject to availability. Unlike widely used growth models based on cellular automata, there are two main advantages of using the proposed machine learning-based framework. Firstly, it does not require to define rules a priori because the model learns the dynamics of growth directly from the historical data. Secondly, it is very easy to train new machine learning models using different explanatory input variables to assess their impact. As a proof of concept, we tested the framework in Valledupar and Rionegro, two Latin American cities located in Colombia with different geomorphological characteristics, and found that the model predictions were in close agreement with the ground-truth based on performance metrics, such as the root-mean-square error, zero-mean normalized cross-correlation, Pearson's correlation coefficient for continuous variables, and a few others for discrete variables such as the intersection over union, accuracy, and the f1 metric. In summary, our framework for modeling urban growth is flexible, allows sensitivity analyses, and can help policymakers worldwide to assess different what-if scenarios during the planning cycle of sustainable and resilient cities. © 2019 by the authors.