Geotechnical Analysis of Contaminated Sand by Light Non-Aquous Phase Liquids

Contaminated soil reuse was investigated, with higher profusion, throughout the early 90’s, coinciding with the 1991 Gulf War, when efforts to amend large crude oil releases began in geotechnical assessment of contaminated soils. Isolated works referring to geotechnical testing with hydrocarbon ground contaminants are described in the state-of-the-art, which have been extended to other type of contaminated soil references. Contaminated soils by light non-aquous phase liquids (LNAPL) bearing capacity reduction has been previously investigated from a forensic point of view. To date, all the research works have been published based on the assumption of constant contaminant saturation for the entire soil mass. In contrast, the actual LNAPLs distribution plumes exhibit complex flow patterns which are subject to physical and chemical changes with time and distance travelled from the release source. This aspect has been considered along the present text. A typical Madrid arkosic soil formation is commonly known as Miga sand. Geotechnical tests have been carried out, with Miga sand specimens, in incremental series of LNAPL concentrations in order to observe the soil engineering properties variation due to a contamination increase. Results are discussed in relation with previous studies and as a matter of fact, soil mechanics parameters change in the presence of LNAPL, showing different tendencies according to each test and depending on the LNAPL content, as well as to the specimen’s initially planned relative density, dense or loose. Geotechnical practical implications are also commented on and analyzed. Variation on geotechnical properties may occur only within the external contour of contamination distribution plume. This scope has motivated the author to develop a physical model based on transparent soil technology. The model aims to reproduce the distribution of LNAPL into the ground due to an accidental release from a storage facility. Preliminary results indicate that the model is a potentially complementary tool for hydrogeological applications, site-characterization and remediation treatment testing within the framework of soil pollution events. A description of the test setup of an innovative three dimensional physical model for the flow of two or more phases, in porous media, is presented herein, along with a summary of the advantages, limitations and future applications for modeling with transparent material. En los primeros años de la década de los años 90, del siglo pasado, coincidiendo con la Guerra del Golfo en 1991, se investigó intensamente sobre la reutilización de suelos afectados por grandes volúmenes de vertidos de crudo, fomentándose la evaluación geotécnica de los suelos contaminados. Se describen, en el estado del arte de esta tésis, una serie de trabajos aislados en relación con la caracterización geotécnica de suelos contaminados con hidrocarburos, descripción ampliada mediante referencias relacionadas con otros tipos de contaminación de suelos. Existen estudios previos de patología de cimentaciones que analizan la reducción de la capacidad portante de suelos contaminados por hidrocarburos líquidos ligeros en fase no acuosa (acrónimo en inglés: LNAPL de “Liquid Non-Aquous Phase Liquid”). A fecha de redacción de la tesis, todas las publicaciones anteriores estaban basadas en la consideración de una saturación del contaminante constante en toda la extensión del terreno de cimentación. La distribución real de las plumas de contaminante muestra, por el contrario, complejas trayectorias de flujo que están sujetas a cambios físico-químicos en función del tiempo y la distancia recorrida desde su origen de vertido. Éste aspecto ha sido considerado y tratado en el presente texto. La arena de Miga es una formación geológica típica de Madrid. En el ámbito de esta tesis se han desarrollado ensayos geotécnicos con series de muestras de arena de Miga contaminadas con distintas concentraciones de LNAPL con el objeto de estimar la variación de sus propiedades geotécnicas debido a un incremento de contaminación. Se ha realizado una evaluación de resultados de los ensayos en comparación con otros estudios previamente analizados, resultando que las propiedades mecánicas del suelo, efectivamente, varían en función del contenido de LNAPL y de la densidad relativa con la que se prepare la muestra, densa o floja. Se analizan y comentan las implicaciones de carácter práctico que supone la mencionada variación de propiedades geotécnicas. El autor ha desarrollado un modelo físico basado en la tecnología de suelos transparentes, considerando que las variaciones de propiedades geotécnicas únicamente deben producirse en el ámbito interior del contorno de la pluma contaminante. El objeto del modelo es el de reproducir la distribución de un LNAPL en un terreno dado, causada por el vertido accidental de una instalación de almecenamiento de combustible. Los resultados preliminares indican que el modelo podría emplearse como una herramienta complementaria para el estudio de eventos contaminantes, permitiendo el desarrollo de aplicaciones de carácter hidrogeológico, caracterización de suelos contaminados y experimentación de tratamientos de remediación. Como aportación de carácter innovadora, se presenta y describe un modelo físico tridimensional de flujo de dos o más fases a través de un medio poroso transparente, analizándose sus ventajas e inconvenientes así como sus limitaciones y futuras aplicaciones.

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours

Microcalcifications Detection using PFCM and ANN

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detection

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Objective: This research is focused in the creation and validation of a solution to the inverse kinematics problem for a 6 degrees of freedom human upper limb. This system is intended to work within a realtime dysfunctional motion prediction system that allows anticipatory actuation in physical Neurorehabilitation under the assisted-as-needed paradigm. For this purpose, a multilayer perceptron-based and an ANFIS-based solution to the inverse kinematics problem are evaluated. Materials and methods: Both the multilayer perceptron-based and the ANFIS-based inverse kinematics methods have been trained with three-dimensional Cartesian positions corresponding to the end-effector of healthy human upper limbs that execute two different activities of the daily life: "serving water from a jar" and "picking up a bottle". Validation of the proposed methodologies has been performed by a 10 fold cross-validation procedure. Results: Once trained, the systems are able to map 3D positions of the end-effector to the corresponding healthy biomechanical configurations. A high mean correlation coefficient and a low root mean squared error have been found for both the multilayer perceptron and ANFIS-based methods. Conclusions: The obtained results indicate that both systems effectively solve the inverse kinematics problem, but, due to its low computational load, crucial in real-time applications, along with its high performance, a multilayer perceptron-based solution, consisting in 3 input neurons, 1 hidden layer with 3 neurons and 6 output neurons has been considered the most appropriated for the target application.

ANN and Fuzzy c-Means applied to environmental pollution prediction

Salamanca, situated in center of Mexico is among the cities which suffer most from the air pollution in Mexico. The vehicular park and the industry, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Sulphur Dioxide (SO2). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables and air pollutant concentrations of SO2. Before the prediction, Fuzzy c-Means and K-means clustering algorithms have been implemented in order to find relationship among pollutant and meteorological variables. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of SO2 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours.

Amazonian white-sand forest: a black future?

Los bosques sobre arena blanca amazónicos son un tipo singular y frágil de bosque tropical húmedo de selva de zonas bajas, que aparecen dispersos sobre teselas de suelo muy oligotrófico y albergan un alto porcentaje de endemismos. No son susceptibles de aprovechamiento maderero ni de uso agrícola, pero su madera redonda de pequeño diámetro (5-15 cm) posee gran durabilidad y es un recurso extraído tradicionalmente por los pobladores locales para construir sus viviendas. A pesar de ello, este aprovechamiento local permanece invisible para la reglamentación forestal, lo que puede perjudicar el futuro de estos bosques sobre arena blanca. Este trabajo tiene por objetivo aportar conocimientos básicos sobre la estructura forestal y la composición florística de estos bosques, lo que es esencial para poder planificar un gestión sustentable. Los resultados muestran que, a pesar de su fragilidad, los bosques sobre arena blanca también presentan ciertas ventajas en vista de sus posibilidades de gestión sustentable en comparación con otro tipo de bosques tropicales húmedos: debido a un alto porcentaje de especies comerciales (26 %), a la clara dominancia de un pequeño grupo de especies, la mayoría de ellas (67%) con interés comercial y al hecho de que los fustes potencialmente aprovechables sólo suponen el 17% del área basimétrica total, resulta que en la situación actual, no es necesario aplicar técnicas de aprovechamiento de impacto reducido, puesto que los pies aprovechados son de pequeño diámetro, que no se utiliza maquinaria y que el transporte se realiza únicamente a hombros y/o por flotación

Dry sand as a specialized layer to improve the acoustic insulation between rooms one above another = La arena como capa intermedia especializada para la mejora del aislamiento acústico entre recintos superpuestos

Se comprueba la mejora en el aislamiento acústico entre recintos de la colocación de una capa de arean entre el solado y el forjado

Analysis of the discharge capacity of radial-gated spillways using CFD and ANN - Oliana Dam case study.

The paper focuses on the analysis of radial-gated spillways, which is carried out by the solution of a numerical model based on the finite element method (FEM). The Oliana Dam is considered as a case study and the discharge capacity is predicted both by the application of a level-set-based free-surface solver and by the use of traditional empirical formulations. The results of the analysis are then used for training an artificial neural network to allow real-time predictions of the discharge in any situation of energy head and gate opening within the operation range of the reservoir. The comparison of the results obtained with the different methods shows that numerical models such as the FEM can be useful as a predictive tool for the analysis of the hydraulic performance of radial-gated spillways.