Examinando por Materia "Losses"

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    Automatic detection of building typology using deep learning methods on street level images
    (PERGAMON-ELSEVIER SCIENCE LTD, 2020-03-20) Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Mecánica Aplicada
    An exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models. © 2020 Elsevier Ltd
  • No hay miniatura disponible
    Ítem
    Automatic detection of building typology using deep learning methods on street level images
    (PERGAMON-ELSEVIER SCIENCE LTD, 2020-03-20) Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)
    An exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models. © 2020 Elsevier Ltd
  • No hay miniatura disponible
    Ítem
    Automatic detection of building typology using deep learning methods on street level images
    (PERGAMON-ELSEVIER SCIENCE LTD, 2020-03-20) Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Universidad EAFIT. Departamento de Ingeniería de Producción; Materiales de Ingeniería
    An exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models. © 2020 Elsevier Ltd
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    Ítem
    Development of a fragility model for the residential building stock in South America
    (EARTHQUAKE ENGINEERING RESEARCH INST, 2017-05-01) Villar-Vega, Mabe; Silva, Vitor; Crowley, Helen; Yepes, Catalina; Tarque, Nicola; Acevedo, Ana Beatriz; Hube, Matias A.; Gustavo, Coronel D.; Maria, Hernan Santa; Mecánica Aplicada
    South America-in particular, the Andean countries-are exposed to high levels of seismic hazard, which, when combined with the elevated concentration of population and properties, has led to an alarming potential for human and economic losses. Although several fragility models have been developed in recent decades for South America, and occasionally used in probabilistic risk analysis, these models have been developed using distinct methodologies and assumptions, which renders any direct comparison of the results across countries questionable, and thus application at a regional level unreliable. This publication aims at obtaining a uniform fragility model for the most representative building classes in the Andean region, for large-scale risk analysis. To this end, sets of single-degree-of-freedom oscillators were created and subjected to a series of ground motion records using nonlinear time history analyses, and the resulting damage distributions were used to derive sets of fragility functions. © 2017, Earthquake Engineering Research Institute.
  • No hay miniatura disponible
    Ítem
    Development of a fragility model for the residential building stock in South America
    (EARTHQUAKE ENGINEERING RESEARCH INST, 2017-05-01) Villar-Vega, Mabe; Silva, Vitor; Crowley, Helen; Yepes, Catalina; Tarque, Nicola; Acevedo, Ana Beatriz; Hube, Matias A.; Gustavo, Coronel D.; Maria, Hernan Santa; Villar-Vega, Mabe; Silva, Vitor; Crowley, Helen; Yepes, Catalina; Tarque, Nicola; Acevedo, Ana Beatriz; Hube, Matias A.; Gustavo, Coronel D.; Maria, Hernan Santa; Universidad EAFIT. Departamento de Ingeniería de Producción; Materiales de Ingeniería
    South America-in particular, the Andean countries-are exposed to high levels of seismic hazard, which, when combined with the elevated concentration of population and properties, has led to an alarming potential for human and economic losses. Although several fragility models have been developed in recent decades for South America, and occasionally used in probabilistic risk analysis, these models have been developed using distinct methodologies and assumptions, which renders any direct comparison of the results across countries questionable, and thus application at a regional level unreliable. This publication aims at obtaining a uniform fragility model for the most representative building classes in the Andean region, for large-scale risk analysis. To this end, sets of single-degree-of-freedom oscillators were created and subjected to a series of ground motion records using nonlinear time history analyses, and the resulting damage distributions were used to derive sets of fragility functions. © 2017, Earthquake Engineering Research Institute.