An Intelligent Classification System For Aggregate Based On Image Processing And Neural Network

Bentuk dan tekstur permukaan aggregat mempengaruhi kekuatan dan struktur konkrit. Secara tradisi, mesin pengayakan mekanikal dan pengukuran manual digunakan bagi menentukan kedua-dua saiz dan bentuk aggregat. Aggregate’s shape and surface texture immensely influence the strength and structure of the resulting concrete. Traditionally, mechanical sieving and manual gauging are used to determine both the size and shape of the aggregates

Strategies of anomalies detection in bridges and tunnels as a tool for structural health management. New approaches and AI support.

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Persistent topology for natural data analysis - A survey

Natural data offer a hard challenge to data analysis. One set of tools is being developed by several teams to face this difficult task: Persistent topology. After a brief introduction to this theory, some applications to the analysis and classification of cells, lesions, music pieces, gait, oil and gas reservoirs, cyclones, galaxies, bones, brain connections, languages, handwritten and gestured letters are shown

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

Automatic detection of pipe-flange reflections in GPR data sections using supervised learning

Ground Penetrating radar (GPR) is a method widely used to study the near-surface subsoil. Many GPR applications require the acquisition of large volumes of data. In these cases, the processing and analysis of the data involve considerable amounts of time and human effort, and the possibility of errors increases. Considering this, the implementation of dependable methods for the automatic detection of GPR response-patterns of the targeted structures becomes clear, because they can contribute to the efficiency and reliability of the interpretation. In this work, we present three methods for automatic detection of pipe-flange signals in constant-offset reflection-GPR images. These methods were obtained using well-known supervised machine learning techniques, and data acquired during a previous study of an extensive section of a pipeline. The first two methods are based on support vector machines (SVM), combined with the image descriptors local binary patterns (LBP) and histogram of oriented gradients (HOG), and the third, on artificial neural networks (ANN). The training and validation of these types of algorithms require large numbers of positive and negative samples. From the mentioned study, we had only 16 experimental flange-patterns. Then, in this work, they were taken as references, together with available documentation on the geometry and materials of the pipe and flanges, for building a broad database of synthetic patterns corresponding to different depths of the pipe and characteristics of the environment. These patterns constitute the set of positive samples used for training and validation. They were also used for the final test of the algorithms. The negative samples for the three stages were directly extracted from the profiles. The results obtained indicate the usefulness of the proposed methodologies to identify the flanges. The best performance corresponded to the ANN, closely followed by SVM combined with HOG, and finally SVM with LBP. In particular, the ANN provided rates of false positive (FP) predictions for the validation and test samples of about 3%, and rates of false negative (FN) predictions of 1.67% for the validation samples and 18.75% for the test samples. Greater FN rates for the test experimental samples, in comparison to those obtained for the validation synthetic samples, were also observed for both SVM algorithms. The detection failures mainly originated in that some complex features of the experimental flange responses could not be appropriately reproduced through the performed numerical simulations, and therefore, some of the patterns were not satisfactorily represented in the sets of positive samples used for training and validation. A first option to improve the results is to obtain a significant number and variety of experimental samples of flange responses and use them to train and validate the algorithms. Other alternatives are to use more sophisticated numerical simulation environments and to find more efficient attributes of the data.Fil: Bordón, Pablo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bonomo, Nestor Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Martinelli, Hilda Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentin

Optimización del diseño estructural de pavimentos asfálticos para calles y carreteras

gráficos, tablasThe construction of asphalt pavements in streets and highways is an activity that requires optimizing the consumption of significant economic and natural resources. Pavement design optimization meets contradictory objectives according to the availability of resources and users’ needs. This dissertation explores the application of metaheuristics to optimize the design of asphalt pavements using an incremental design based on the prediction of damage and vehicle operating costs (VOC). The costs are proportional to energy and resource consumption and polluting emissions. The evolution of asphalt pavement design and metaheuristic optimization techniques on this topic were reviewed. Four computer programs were developed: (1) UNLEA, a program for the structural analysis of multilayer systems. (2) PSO-UNLEA, a program that uses particle swarm optimization metaheuristic (PSO) for the backcalculation of pavement moduli. (3) UNPAVE, an incremental pavement design program based on the equations of the North American MEPDG and includes the computation of vehicle operating costs based on IRI. (4) PSO-PAVE, a PSO program to search for thicknesses that optimize the design considering construction and vehicle operating costs. The case studies show that the backcalculation and structural design of pavements can be optimized by PSO considering restrictions in the thickness and the selection of materials. Future developments should reduce the computational cost and calibrate the pavement performance and VOC models. (Texto tomado de la fuente)La construcción de pavimentos asfálticos en calles y carreteras es una actividad que requiere la optimización del consumo de cuantiosos recursos económicos y naturales. La optimización del diseño de pavimentos atiende objetivos contradictorios de acuerdo con la disponibilidad de recursos y las necesidades de los usuarios. Este trabajo explora el empleo de metaheurísticas para optimizar el diseño de pavimentos asfálticos empleando el diseño incremental basado en la predicción del deterioro y los costos de operación vehicular (COV). Los costos son proporcionales al consumo energético y de recursos y las emisiones contaminantes. Se revisó la evolución del diseño de pavimentos asfálticos y el desarrollo de técnicas metaheurísticas de optimización en este tema. Se desarrollaron cuatro programas de computador: (1) UNLEA, programa para el análisis estructural de sistemas multicapa. (2) PSO-UNLEA, programa que emplea la metaheurística de optimización con enjambre de partículas (PSO) para el cálculo inverso de módulos de pavimentos. (3) UNPAVE, programa de diseño incremental de pavimentos basado en las ecuaciones de la MEPDG norteamericana, y el cálculo de costos de construcción y operación vehicular basados en el IRI. (4) PSO-PAVE, programa que emplea la PSO en la búsqueda de espesores que permitan optimizar el diseño considerando los costos de construcción y de operación vehicular. Los estudios de caso muestran que el cálculo inverso y el diseño estructural de pavimentos pueden optimizarse mediante PSO considerando restricciones en los espesores y la selección de materiales. Los desarrollos futuros deben enfocarse en reducir el costo computacional y calibrar los modelos de deterioro y COV.DoctoradoDoctor en Ingeniería - Ingeniería AutomáticaDiseño incremental de pavimentosEléctrica, Electrónica, Automatización Y Telecomunicacione

How soft materials control harder ones: routes to bioorganization

Ordered structures are remarkably common, even without direct human guidance or direction. The ordering can be at the atomic scale or on the macroscopic scale or at the mesoscale. The term 'self-organization' is often used, but this description is facile, giving no hint as to the range or variety of mechanisms. Ordering can occur in circumstances commonly associated with disorder, as in the irradiation of metals to high doses; it can also occur when soft, flexible materials organize structures of harder, rigid structures. My review attempts to analyse some of these widely varying behaviours, both to seek evidence of common underlying principles and to assess how organization might be controlled, and with what level of accuracy