Aplicación de la regresión logística en la predicción empírica de fenómenos complejos en obras subterráneas : squeezing y rotura de pilares de carbón

La heterogeneidad del medio geológico introduce en el proyecto de obra subterránea un alto grado de incertidumbre que debe ser debidamente gestionado a fin de reducir los riesgos asociados, que son fundamentalmente de tipo geotécnico. Entre los principales problemas a los que se enfrenta la Mecánica de Rocas moderna en el ámbito de la construcción subterránea, se encuentran la fluencia de roca en túneles (squeezing) y la rotura de pilares de carbón. Es ampliamente conocido que su aparición causa importantes perjuicios en el coste y la seguridad de los proyectos por lo que su estudio, ha estado tradicionalmente vinculado a la predicción de su ocurrencia. Entre las soluciones existentes para la determinación de estos problemas se encuentran las que se basan en métodos analíticos y numéricos. Estas metodologías son capaces de proporcionar un alto nivel de representatividad respecto del comportamiento geotécnico real, sin embargo, su utilización solo es posible cuando se dispone de una suficiente caracterización geotécnica y por tanto de una detallada definición de los parámetros que alimentan los complejos modelos constitutivos y criterios de rotura que los fenómenos estudiados requieren. Como es lógico, este nivel de definición solo es posible cuando se alcanzan etapas avanzadas de proyecto, incluso durante la propia construcción, a fin de calibrar adecuadamente los parámetros introducidos en los modelos, lo que supone una limitación de uso en etapas iniciales, cuando su predicción tiene verdadero sentido. Por su parte, los métodos empíricos permiten proporcionar soluciones a estos complejos problemas de un modo sencillo, con una baja parametrización y, dado su eminente enfoque observacional, de gran fiabilidad cuando se implementan sobre condiciones de contorno similares a las originales. La sencillez y escasez de los parámetros utilizados permiten a estas metodologías ser utilizadas desde las fases preliminares del proyecto, ya que estos constituyen en general, información habitual de fácil y económica adquisición. Este aspecto permite por tanto incorporar la predicción desde el principio del proceso de diseño, anticipando el riesgo en origen. En esta tesis doctoral, se presenta una nueva metodología empírica que sirve para proporcionar predicciones para la ocurrencia de squeezing y el fallo de pilares de carbón basada en una extensa recopilación de información de casos reales de túneles y minas en las que ambos fenómenos fueron evaluados. Esta información, recogida de referencias bibliográficas de prestigio, ha permitido recopilar una de las más extensas bases de datos existentes hasta la fecha relativa a estos fenómenos, lo que supone en sí mismo una importante contribución sobre el estado del arte. Con toda esta información, y con la ayuda de la teoría de clasificadores estadísticos, se ha implementado sobre las bases de datos un clasificador lineal de tipo regresión logística que permite hacer predicciones sobre la ocurrencia de ambos fenómenos en términos de probabilidad, y por tanto ponderar la incertidumbre asociada a la heterogeneidad incorporada por el medio geológico. Este aspecto del desarrollo es el verdadero valor añadido proporcionado por la tesis y la principal ventaja de la solución propuesta respecto de otras metodologías empíricas. Esta capacidad de ponderación probabilística permite al clasificador constituir una solución muy interesante como metodología para la evaluación de riesgo geotécnico y la toma de decisiones. De hecho, y como ejercicio de validación práctica, se ha implementado la solución desarrollada en un modelo coste-beneficio asociado a la optimización del diseño de pilares involucrados en una de mina “virtual” explotada por tajos largos. La capacidad del clasificador para cuantificar la probabilidad de fallo del diseño, junto con una adecuada cuantificación de las consecuencias de ese fallo, ha permitido definir una ley de riesgo que se ha incorporado al balance de costes y beneficios, que es capaz, a partir del redimensionamiento iterativo del sistema de pilares y de la propia configuración de la mina, maximizar el resultado económico del proyecto minero bajo unas condiciones de seguridad aceptables, fijadas de antemano. Geological media variability introduces to the subterranean project a high grade of uncertainty that should be properly managed with the aim to reduce the associated risks, which are mainly geotechnical. Among the major problems facing the modern Rock Mechanics in the field of underground construction are both, the rock squeezing while tunneling and the failure of coal pillars. Given their harmfulness to the cost and safety of the projects, their study has been traditionally linked to the determination of its occurrence. Among the existing solutions for the determination of these problems are those that are based on analytical and numerical methods. Those methodologies allow providing a high level of reliability of the geotechnical behavior, and therefore a detailed definition of the parameters that feed the complex constitutive models and failure criteria that require the studied phenomena. Obviously, this level of definition is only possible when advanced stages of the project are achieved and even during construction in order to properly calibrate the parameters entered in the models, which suppose a limited use in early stages, when the prediction has true sense. Meanwhile, empirical methods provide solutions to these complex problems in a simple way, with low parameterization and, given his observational scope, with highly reliability when implemented on similar conditions to the original context. The simplicity and scarcity of the parameters used allow these methodologies be applied in the early stages of the project, since that information should be commonly easy and cheaply to get. This aspect can therefore incorporate the prediction from the beginning of the design process, anticipating the risk beforehand. This thesis, based on the extensive data collection of case histories of tunnels and underground mines, presents a novel empirical approach used to provide predictions for the occurrence of both, squeezing and coal pillars failures. The information has been collected from prestigious references, providing one of the largest databases to date concerning phenomena, a fact which provides an important contribution to the state of the art. With all this information, and with the aid of the theory of statistical classifiers, it has been implemented on both databases, a type linear logistic regression classifier that allows predictions about the occurrence of these phenomena in terms of probability, and therefore weighting the uncertainty associated with geological variability. This aspect of the development is the real added value provided by the thesis and the main advantage of the proposed solution over other empirical methodologies. This probabilistic weighting capacity, allows being the classifier a very interesting methodology for the evaluation of geotechnical risk and decision making. In fact, in order to provide a practical validation, we have implemented the developed solution within a cost-benefit analysis associated with the optimization of the design of coal pillar systems involved in a "virtual" longwall mine. The ability of the classifier to quantify the probability of failure of the design along with proper quantification of the consequences of that failure, has allowed defining a risk law which is introduced into the cost-benefits model, which is able, from iterative resizing of the pillar system and the configuration of the mine, maximize the economic performance of the mining project under acceptable safety conditions established beforehand.

GovCloud: the usage of cloud computing in public organizations

It has been shown that cloud computing brings cost benefits and promotes efficiency in the operations of the organizations, no matter what their type or size. However, few public organizations are benefiting from this new paradigm shift in the way the organizations consume and manage computational resources. The objective of this thesis is to analyze both internal and external factors that may influence the adoption of cloud computing by public organizations and propose possible strategies that can assist these organizations in their path to cloud usage. In order to achieve this objective, a SWOT analysis has been conducted, detecting internal factors (strengths and weaknesses) and external factors (opportunities and threats) that can influence the adoption of a governmental cloud. With the application of a TOWS matrix, by combining the internal and external factors, a list of possible strategies have been formulated to be used as a guide to decision-making related to the transition to a cloud environment.

Cover crops effect on farm benefits and nitrate leaching: Linking economic and environmental analysis.

Introducing cover crops (CC) interspersed with intensively fertilized crops in rotation has the potential to reduce nitrate leaching. This paper evaluates various strategies involving CC between maize and compares the economic and environmental results with respect to a typical maize?fallow rotation. The comparison is performed through stochastic (Monte-Carlo) simulation models of farms? profits using probability distribution functions (pdfs) of yield and N fertilizer saving fitted with data collected from various field trials and pdfs of crop prices and the cost of fertilizer fitted from statistical sources. Stochastic dominance relationships are obtained to rank the most profitable strategies from a farm financial perspective. A two-criterion comparison scheme is proposed to rank alternative strategies based on farm profit and nitrate leaching levels, taking the baseline scenario as the maize?fallow rotation. The results show that when CC biomass is sold as forage instead of keeping it in the soil, greater profit and less leaching of nitrates are achieved than in the baseline scenario. While the fertilizer saving will be lower if CC is sold than if it is kept in the soil, the revenue obtained from the sale of the CC compensates for the reduced fertilizer savings. The results show that CC would perhaps provide a double dividend of greater profit and reduced nitrate leaching in intensive irrigated cropping systems in Mediterranean regions.

Identifying Potential Risks and Benefits of Using Cloud in Distributed Software Development.

Cloud-based infrastructure has been increasingly adopted by the industry in distributed software development (DSD) environments. Its proponents claim that its several benefits include reduced cost, increased speed and greater productivity in software development. Empirical evaluations, however, are in the nascent stage of examining both the benefits and the risks of cloud-based infrastructure. The objective of this paper is to identify potential benefits and risks of using cloud in a DSD project conducted by teams based in Helsinki and Madrid. A cross-case qualitative analysis is performed based on focus groups conducted at the Helsinki and Madrid sites. Participants observations are used to supplement the analysis. The results of the analysis indicated that the main benefits of using cloud are rapid development, continuous integration, cost savings, code sharing, and faster ramp-up. The key risks determined by the project are dependencies, unavailability of access to the cloud, code commitment and integration, technical debt, and additional support costs. The results revealed that if such environments are not planned and set up carefully, the benefits of using cloud in DSD projects might be overshadowed by the risks associated with it.