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Numerical simulation of cone penetration tests in loose unsaturated soils
No full textCone penetration tests (CPTu) are frequently performed in soils that are not fully saturated. Test results are strongly affected by partial saturation but it is not well understood how those changes come about and how are they related to unsaturated soil behavior. A special difficulty relates to water flow conditions around the probe, which are affected not just by intrinsic soil permeability but also by the unsaturated soil condition. Numerical simulation is here employed to explore the relation between partly saturated soil conditions and CPTu measurements. Simulations employ the particle finite element method (PFEM) to represent cone penetration in a material described by a finite strain elasto-plastic critical state model extended to partly saturated conditions. Model parameters are chosen to represent loose soils of low plasticity. A systematic parametric study is carried out to explore the effect of suction levels and soil intrinsic permeability on cone response. Backbone curves for changes in tip resistance, sleeve friction and recorded pore pressure with normalized cone velocity are presented. It is shown how the transition between constant suction and constant water content conditions generalizes the drained to undrained transition of saturated soils. The effect of initial suction, water retention and suction hardening on the backbone curves is presented. The conditions in which an advancing cone re-saturates the soil and records positive pore pressure are clarified.The authors gratefully acknowledge financial support from the Spanish Ministry of Science, Innovation and Universities (MICIU/AEI/10.13039/501100011033) and the European Union (ERDF/EU) through research project PID2023-149935OB-I00.Peer ReviewedPostprint (published version
Travel demand & Behavioral Modeling. Topic 2: Data and Space
No full textSlides aboutthe topic2025/20262n quadrimestr
Algorísmia (Examen Final, 1r Quadrimestre )
No full textExamen Final 1er QuadrimestreResolved2025/20261r quadrimestr
Group-based trajectory modeling to identify homogeneous latent classes for HIV patients
No full textEl modelatge de trajectòries basat en grup (GBTM) és un mètode semiparamètric basat en dades longitudinals que s'utilitzen per identificar trajectòries d'una variable d'interès entre individus i examinar el seguiment en el seu conjunt. GBTM suposa que no hi ha variació entre individus de la mateixa classe. D'aquesta manera, els individus amb trajectòries lleugerament diferents es classifiquen més fàcilment en la mateixa classe. L'objectiu principal d'aquest projecte és ajustar i avaluar les trajectòries i les característiques dels pacients amb VIH d'un conjunt de dades longitudinals (LAKE) recollit en un assaig clínic. El paquet R lcmm basat en la teoria de models lineals mixtes s'utilitzarà per executar aquest tipus de models.El modelado de trayectorias basado en grupos (GBTM) es un método semiparamétrico basado en datos longitudinales que se utilizan para identificar trayectorias de una variable de interés entre individuos y examinar el seguimiento en su conjunto. GBTM supone que no hay variación entre individuos de la misma clase. De este modo, los individuos con trayectorias ligeramente diferentes se clasifican más fácilmente en la misma clase. El objetivo principal de este proyecto es ajustar y evaluar trayectorias y características de pacientes con VIH de un conjunto de datos longitudinales (LAKE) recopilados en un ensayo clínico. Para ejecutar este tipo de modelos se utilizará el paquete R lcmm basado en la teoría de modelos lineales mixtos.Group-based trajectory modeling (GBTM) is a semi-parametric method based on longitudinal data used to identify trajectories of a variable of interest across individuals and examine the follow-up as a whole. GBTM assumes that there is no variation across individuals in the same class. Thereby, individuals with slightly different trajectories are more easily classified into the same class. The main aim of this project is to fit and to assess trajectories and characteristics of HIV patients of a longitudinal data set (LAKE) collected in a clinical trial. The R package lcmm based on linear mixed model theory will be used to run these types of models
L'Atelier au-delà du lieu : le projet d’architecture au sein d’un dispositif pédagogique et spatial : étude comparée ENSAM ETSAV
No full textL’école d’architecture, en tant que lieu d’enseignement d’un savoir spatial, semble être un terrain privilégié pour interroger la pédagogie au travers de la forme, de l’usage et des ambiances qui s’en dégagent, la recherche entend donc se concentrer sur les lieux spécifiques à cette formation. C’est sur ces intuitions et sur l’observation subjective d’une évolution asynchrone des réformes pédagogiques de la formation en architecture et des modifications spatiales de ses lieux d’enseignement que prend naissance cette étude. Ce travail vise également, en partie, à analyser l’influence que peut avoir l’espace sur les échanges et interactions cognitives. En particulier dans l’enseignement en groupe tel que celui de l’architecture. Il implique donc de ne pas se limiter à la discipline de l’architecture et de tendre vers d’autres domaines tel que celui de l’anthropologie04 afin de saisir pleinement les usages et pratiques qui se développent au sein des structures étudiées. Deux cas d’études se distingueront d’un corpus plus élargi afin d’en réaliser une analyse plus sensible et approfondie : L’ENSAM
et l’ETSAV. Leurs sélections découlant de l’immersion parallèle menée au sein des deux établissements tout au long de la rédaction de ce mémoire permettant une démarche plus empirique par une collecte de donnée plus située
A regional-scale early warning system for rainfall-induced shallow landslides based on the outputs of a physically based model: application to Cili County, China
No full textThis paper presents a new method for a regional-scale rainfall-induced landslide early warning system (LEWS) based on the outputs of the “Fast Shallow Landslide Assessment Model” (FSLAM), a physically based model used to compute slope stability at a regional scale. The LEWS combines landslide susceptibility and rainfall thresholds to depict the areas that are prone to slope failures and issues qualitative warnings over the study area. Both the susceptibility map and the rainfall thresholds were obtained based on the outputs from running FSLAM with 25 different rainfall scenarios. The final output of the LEWS is a slope-unit-based map. The LEWS was implemented for Cili County, Hunan Province, China, and tested for the year 2020. The warning level stayed “Low” during most of the year. High warnings were issued during the summer and were either due to intense rainfall events or abundant long-duration precipitation. The LEWS was able to issue appropriate warnings corresponding to the time and location of three known landslides that occurred in the study area in 2020. Although long-term validation with more landslide data and improved geotechnical data is needed to reduce the LEWS uncertainties, this approach is promising and could support authorities managing landslide risk.This research is supported by the China Scholarship Council (No. 20210641003).Peer ReviewedPostprint (published version
Scalable energy-aware VM allocation on cloud data centers through mathematical programming models
No full textCloud data centers are becoming indispensable pillars of modern society, driving AI innovation, global connectivity, and data-driven advancements. As their size and complexity grow, so does the urgency for sustainable and efficient solutions to address operational and environmental challenges. The Virtual Machine (VM) allocation problem lies at the heart of these challenges, directly impacting energy consumption, scalability, and cost-effectiveness. While heuristics are traditionally favored for their fast execution times, they fail to adequately address the complexities of heterogeneous environments and the increasing need for energy-aware solutions. In this work, we redefine the potential of mathematical programming models — traditionally considered impractical due to scalability limitations — by defining a comprehensive VM allocation strategy that embeds the models into scalable algorithms that distribute computational workloads and exploit solver capabilities. This approach achieves linear scalability — an unprecedented milestone for mathematical programming — allowing us to integrate detailed and heterogeneous aspects of the VM allocation problem. The resulting algorithms dramatically outperform state-of-the-art heuristics and metaheuristics in both scalability and solution quality, delivering an average 16% increase in Net Profit, a 54% reduction in Total Energy Consumption, and a more-than-double improvement in Energy Efficiency. Designed to meet the evolving demands of modern Cloud data centers, our algorithms scale efficiently to manage growing workloads, adapt to heterogeneity, and comply with sustainability and regulatory requirements by prioritizing energy efficiency, facilitating the transition to next-generation Cloud environments.This research was partially supported by the EU-HORIZON programme under grant agreement EU-HORIZON GA.101092646, by the Spanish Ministry of Science and the Research State Agency (MICIU/AEI/ 10.13039/501100011033) and by European Regional Development Funds (ERDF/FEDER) under contract PID2021-126248OB-I00, and by the Generalitat de Catalunya (AGAUR) under contract 2021-SGR-00478.Peer ReviewedPostprint (published version
RV.S8.DR.V2 Perspectiva y renderización
No full textVídeos docents de l'assignatura Representació Arquitectònica II (REVIT) impartida per Isidro Navarro, PDI de l'ETSA