PROBABILISTIC CALIBRATION OF A SOIL MODEL

A constitutive model is a relationship between material stimuli and responses. Calibration of model parameters within well-defined constitutive models is thus key to the generation of accurate model-based predictions. One limitation of traditional material calibration is that only a few standardized tests are performed for estimating constitutive parameters, which makes the calibration process eminently deterministic. Moreover, measurements taken during standardized tests are usually global readings, which implicitly assume a ‘homogeneous’ material composition, smearing out the influence of any local effects. This work introduces the Functional Bayesian (FB) formulation as a probabilistic methodology for the calibration of constitutive models that incorporates material random responses and local effects into the assessment of constitutive parameters. This particular calibration process is known as the probabilistic solution to the inverse problem. Estimates of the statistics required for the Bayesian solution are obtained from a series of standard triaxial tests which are coupled with 3-Dimensional (3D) stereo digital images allowing for the capturing of material local effects. In addition, the probabilistic method includes the spatial representation of elemental ‘material’ properties by introducing spatially varying parameters within a 3D Finite Element Model (3D-FEM) to reproduce to the extent possible the actual heterogeneous response of the material. The sampling of spatial ‘material’ realizations is performed by the Polynomial Chaos (PC) method, which permits the simulation of multi-dimensional non-Gaussian and non-stationary random fields. Integration of the random parameters is performed via Markov Chain Monte-Carlo and Metropolis-Hastings algorithms. The calibration of a soil iii sample is presented as a case study to illustrate the applicability of the method when the soil response lies within the linear elastic domain. Calibration results show a probabilistic description of the spatially distributed parameters and of the coefficients of the chaos representation that defines it. Inferences retrieved from the MCMC sampling include the analysis of the ‘material’ properties and of the coefficients of the PC representation which enhances understanding of the randomness associated with the material composition and response

Probabilistic calibration of avalanche block models.

The Norwegian Geotechnical Institute (NGI) has been operating the full-scale avalanche test-site Ryggfonn in western Norway for more than 25 years. During those years, measurements from about three dozen dry-snow avalanches have provided information on front velocities and runout distances. Some of those measurements were used to calibrate a simple avalanche model following a well-defined probabilistic method.. Traditionally, model parameters of those kinds of models were evaluated from runout analysis disregarding any dynamics. The set of roughly 20 observed avalanches from one single path including, estimations of the front velocities at three points in the lower third of the track provided a unique opportunity for introducing uncertainty quantification methods for evaluating the performance of similar kind of competing models. We present the model calibration and results from the model performance testing

Actionable Information - Research Briefs - 3 - Analysis on U.S. States COVID-19 Dashboards

Includes both static PDF version and the dynamic web version.Covid-19 dashboards developed by state authorities in the U.S. present different variables to communicate the status and evolution of the pandemic in their territories. This research brief summarizes the different platforms used to develop the dashboards. Also, the brief includes an analysis on the dashboard contents in terms of total number of variables, type of variables, number of variables per risk component (i.e. Threats, Vulnerable Systems, Impacts, States of Risk, Mitigating Strategies). Finally, the number of variables per risk component are compared to COVID-19 metrics such as daily cases, and daily deaths (per 100K population) in order to identify how the risk communication in the dashboards impact the management of the pandemic

Actionable Information - Research Briefs - 2 - U.S. and Mexico COVID-19 Dashboards

Includes both static PDF version and the dynamic web version.States in the U.S. have produced COVID-19 dashboards to communicate the status of the pandemic to the population. This research brief presents a summary of the available dashboards for the U.S. and Mexico states. Additionally a map of the U.S. is presented with the total number of variables that are reported in the state dashboard(s)

Actionable Information - Research Briefs - 5 - Summary and Assessment of Weather Information Services

Includes both static PDF version and the dynamic web version.This research brief includes a summary of various weather information services, detailing the procedures followed to obtain and generate data, and a breakdown of the information provided. Based on this information, the services were qualitatively assessed to determine the most fitting one (s) in the production of risk analytics for supply chains impacted by natural Threat

Actionable Information - Research Briefs - 4 - Literature Review on the Impact of Natural Threats on Supply Chains

Includes both static PDF version and the dynamic web version.A literature review was conducted in order to identify the most impactful effects of natural hazards on the operation of supply chains. The review considered the body of research produced about this topic including the distribution of scientific documents produced by year, and the number of mentions of different natural threats in them

R7 - Internal Report on Risk Assessment & Management Model Development V0.0

Internal report on the development steps for a Risk Assessment and Management model using Bayesian Networks. The objectives of the model include: mapping qualitatively participating processes needed to simulate prognosis and diagnosis scenarios of social, economic and environmental impacts posed by COVID19 on the U.S. trade supply chain infrastructure. To address the public health impacts of the COVID-19 pandemic on the U.S.- Mexico health supply chain systems for health infrastructure and for the health of the workforce, considering current and emerging regional social, economic and environmental Risks. To generate risk-mitigating strategies based on resiliency and sustainability supported by evidence collection and the associate risk assessment model, to address causes and effects posed by COVID19 on the U.S. trade supply chain infrastructure, U.S.- Mexico health supply chain systems for health infrastructure, and for health of the workforce between U.S. - Mexico

R13 - U.S.-Mexico Taskforce to Support the Health Supply Chain Systems for Infrastructure and Workforce Threatened by the COVID19 Pandemic

The project's milestones include the integration of a triple-helix Binational Taskforce, production of spatio-temporal near real-time analytics following a risk systems approach, and publication of a monthly U.S.-Mexico COVID-19 Risk bulletin

Actionable Information - Research Briefs - Vaccination

Includes both static PDF version and the dynamic web version.Five research problems to identify evidence sources and provide initial validation of the risk framework model and data lake have been identified. We focused on COVID-19 Vaccination in the United States and Mexico as the predominant mitigating action. In addition to reliable sources of information, we've identified preordered vaccine supply, its main supply chain elements, and the critical facilities involved in the manufacturing and distribution of millions of doses. In this document we present an initial assessment and findings for this mitigating action