Thermo-hydro-mechanical responses of the host rock in the context of geological nuclear waste disposal

Deep geological disposal facility has been considered as the most appropriated solution for the safe longterm management of high-level radioactive waste (HLW). Geologic disposal solution consists of isolating the radioactive waste from the biosphere. Argillaceous rock has been selected in several countries as host formation due to its favorable properties to isolate radionuclides and chemical contaminants (very low permeability, stable, high retention capacity, self-sealing, etc). Clays in their natural state is usually saturated. Disposal of the exothermic waste packages in the repository leads to an increase in temperature within the host rock, which induces the pore pressure build-up due to the difference in thermal expansion coefficients of the pore water and the solid skeleton. The excess pore pressure generally leads to a decrease in the effective stress and can provoke thermally hydraulic fracturing or shear failure. Therefore, understanding the thermo-hydro-mechanical (THM) responses of the saturated host rock due to the heat generated form waste packages is a key issue to assess the feasibility of the repository. This paper aims at presenting coupled THM constitutive equations for a saturated porous medium and its finite element (FEM) discretization and solution. The solution is validated against analytical solution and other numerical results from a benchmark within an international project. FEM program is then used to describe the THM behavior of the host rock around a HLW repository (i.e. near field responses). Sensitivity analysis were performed to evaluate effect of material anisotropy and hydraulic condition on the micro-tunnel wall

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024