Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

Amezquita-Sanchez, Juan P.; Valtierra-Rodríguez, Martín; Adeli, Hojjat

doi:10.24200/sci.2017.4589

Cited by 7 publications

(3 citation statements)

References 163 publications

(184 reference statements)

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“…Application of Bayesian modeling in flood vulnerability analysis is motivated by the potential of Bayesian inference to compute temporal flow rates in a basin. Flow rates are usually determined using rainfall‐runoff (e.g., Xinanjiang model (Lü et al., )) and streamflow forecasting models, as well as other required parameters, such as soil properties and topological structure of the channel network (Amezquita‐Sanchez, Valtierra‐Rodriguez, & Adeli, ). In these hydrology‐based models, a set of parameters, such as rainfall, water elevation, evaporation rate, water capacity, and coefficients of transition, are used as the input to Bayesian models.…”

Section: Literature Reviewmentioning

confidence: 99%

Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

Dong

Farahmand

et al. 2019

Computer aided Civil Eng

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This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision-makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near-real-time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas. 668 wileyonlinelibrary.com/journal/mice Comput Aided Civ Inf. 2020;35:668-684.

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Section: Literature Reviewmentioning

confidence: 99%

Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

Dong

Farahmand

et al. 2019

Computer aided Civil Eng

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“…In earthquake‐prone areas, existing structures (especially those designed according to pre‐seismic codes) are often incapable of sustaining severe earthquake‐induced structural/non‐structural demands. After significant earthquake events, this may likely result in many casualties and vast economic losses (both direct and indirect), with respect to other hazards (e.g., Amezquita‐Sanchez et al., 2017). In general terms, seismic risk mitigation can be achieved, for instance, by either implementing structural retrofit strategies that reduce the physical seismic vulnerability/expected damage of buildings (hard solutions) and/or by transferring the risk to the (re)insurance market (soft solutions), among other techniques, such as earthquake early warning (e.g., Cremen & Galasso, 2021).…”

Section: Introductionmentioning

confidence: 99%

A computational framework for selecting the optimal combination of seismic retrofit and insurance coverage

Gentile

Pampanin

Galasso

2021

Computer aided Civil Eng

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Economic earthquake losses can be mitigated through either building retrofit strategies and/or, to some extent, risk-transfer to the (re)insurance market. This paper proposes a computational framework to select the optimal combination of seismic retrofit and insurance policy parameters for buildings. First, a designer selects a suitable retrofit strategy. This is implemented incrementally to define interventions with increasing retrofit performance levels. The cost of each intervention is calculated, along with the cost of property rental while the retrofit is implemented. Alternative insurance options are considered. For each retrofit-insurance combination, the insured and uninsured economic losses within a given time horizon are estimated. The optimal retrofit and insurance combination minimizes the tail value at risk of the life cycle cost. The selected confidence level for this metric depends on the homeowner's risk aversion. The proposed framework is illustrated for a case-study archetype Italian reinforced concrete frame building retrofitted with concrete jacketing, also considering the Italian retrofit tax incentives/rebates called "Sismabonus."

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“…In this regard, this type of design has been used to construct bridges, towers, cranes, roof supports, and building skeletons, among others [3][4][5]. Nevertheless, they suffer continuous degradation or failure during their service life because of excitations produced by human or natural hazards, such as earthquakes, tornadoes, hurricanes, and wind, among others [6][7][8]. Hence, a continuous assessment of their structural integrity, known as structural health monitoring (SHM), is of paramount importance, since any deterioration or failure can be attended to early, in order to avoid potential human and economic losses.…”

Section: Introductionmentioning

confidence: 99%

EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure

et al. 2018

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Truss-type designs are widely used in civil structures. Despite the fact that they are robust and reliable structures, different kinds of damage can appear. In order to avoid human and economic losses, the development and application of damage-detection methodologies are paramount. In this work, a methodology based on the empirical mode decomposition (EMD) method and the Shannon Entropy Index (SEI) to detect incipient damages associated with corrosion in a 3D 9-bay truss-type bridge is presented. As different EMD methods are presented in literature, the most representative methods are investigated in order to evaluate their performance for this task. To this end, the vibration signals generated in the truss-type bridge at different conditions are analyzed. For the damage condition, four severity levels of simulated corrosion (1 mm, 3 mm, 5 mm, and 8 mm of diameter reduction) generated into the elements of truss-type bridge are considered. Results demonstrate the effectiveness of the proposal in terms of detecting corrosion in its very early stage (1 mm of reduction in the element).

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Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

Cited by 7 publications

References 163 publications

Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

A computational framework for selecting the optimal combination of seismic retrofit and insurance coverage

EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure

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