1,656 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Review of the state of practice in geovisualization in the geosciences

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    Geosciences modelling and 3D geovisualization is growing and evolving rapidly. Driven by commercial urgency and an increase in data from sensor-based sources, there is an abundance of opportunities to analyze geosciences data in 3D and 4D. Geosciences modelling is developing in GIS based systems, 3D modelling through both game engines and custom programs, and the use of extended reality to further interact with data. The key limitations that are currently prevalent in 3D geovisualization in the geosciences are GIS representations having difficulty displaying 3D data and undergoing translations to pseudo-3D, thus losing fidelity, financial and personnel capital, processing issues with the terabytes worth of data and limited computing, digital occlusion and spatial interpretation challenges with users, and matching and alignment of 3D points. The future of 3D geovisualization lies in its accelerated growth, data management solutions, further interactivity in applications, and more information regarding the benefits and best practices in the field

    Undergraduate Catalog of Studies, 2023-2024

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    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    Undergraduate Catalog of Studies, 2022-2023

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    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Towards an integrated vulnerability-based approach for evaluating, managing and mitigating earthquake risk in urban areas

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    Tese de doutoramento em Civil EngineeringSismos de grande intensidade, como aqueles que ocorreram na Turquía-Síria (2023) ou México (2017) deviam chamar a atenção para o projeto e implementação de ações proativas que conduzam à identificação de bens vulneráveis. A presente tese propõe um fluxo de trabalho relativamente simples para efetuar avaliações da vulnerabilidade sísmica à escala urbana mediante ferramentas digitais. Um modelo de vulnerabilidade baseado em parâmetros é adotado devido à afinidade que possui com o Catálogo Nacional de Monumentos Históricos mexicano. Uma primeira implementação do método (a grande escala) foi efetuada na cidade histórica de Atlixco (Puebla, México), demonstrando a sua aplicabilidade e algumas limitações, o que permitiu o desenvolvimento de uma estratégia para quantificar e considerar as incertezas epistémicas encontradas nos processos de aquisição de dados. Devido ao volume de dados tratado, foi preciso desenvolver meios robustos para obter, armazenar e gerir informações. O uso de Sistemas de Informação Geográfica, com programas à medida baseados em linguagem Python e a distribuição de ficheiros na ”nuvem”, facilitou a criação de bases de dados de escala urbana para facilitar a aquisição de dados em campo, os cálculos de vulnerabilidade e dano e, finalmente, a representação dos resultados. Este desenvolvimento foi a base para um segundo conjunto de trabalhos em municípios do estado de Morelos (México). A caracterização da vulnerabilidade sísmica de mais de 160 construções permitiu a avaliação da representatividade do método paramétrico pela comparação entre os níveis de dano teórico e os danos observados depois do terramoto de Puebla-Morelos (2017). Esta comparação foi a base para efetuar processos de calibração e ajuste assistidos por algoritmos de aprendizagem de máquina (Machine Learning), fornecendo bases para o desenvolvimento de modelos de vulnerabilidade à medida (mediante o uso de Inteligência Artificial), apoiados nas evidências de eventos sísmicos prévios.Strong seismic events like the ones of Türkiye-Syria (2023) or Mexico (2017) should guide our attention to the design and implementation of proactive actions aimed to identify vulnerable assets. This work is aimed to propose a suitable and easy-to-implement workflow for performing large-scale seismic vulnerability assessments in historic environments by means of digital tools. A vulnerability-oriented model based on parameters is adopted given its affinity with the Mexican Catalogue of Historical Monuments. A first large-scale implementation of this method in the historical city of Atlixco (Puebla, Mexico) demonstrated its suitability and some limitations, which lead to develop a strategy for quantifying and involving the epistemic uncertainties found during the data acquisition process. Given the volume of data that these analyses involve, it was necessary to develop robust data acquisition, storing and management strategies. The use of Geographical Information System environments together with customised Python-based programs and cloud-based distribution permitted to assemble urban databases for facilitating field data acquisition, performing vulnerability and damage calculations, and representing outcomes. This development was the base for performing a second large-scale assessment in selected municipalities of the state of Morelos (Mexico). The characterisation of the seismic vulnerability of more than 160 buildings permitted to assess the representativeness of the parametric vulnerability approach by comparing the theoretical damage estimations against the damages observed after the Puebla-Morelos 2017 Earthquakes. Such comparison is the base for performing a Machine Learning assisted process of calibration and adjustment, representing a feasible strategy for calibrating these vulnerability models by using Machine-Learning algorithms and the empirical evidence of damage in post-seismic scenarios.This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), reference UIDB/04029/2020. This research had financial support provided by the Portuguese Foundation of Science and Technology (FCT) through the Analysis and Mitigation of Risks in Infrastructures (InfraRisk) program under the PhD grant PD/BD/150385/2019

    Assessing compounding risks across multiple systems and sectors: a socio-environmental systems risk-triage approach

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    Physical and societal risks across the natural, managed, and built environments are becoming increasingly complex, multi-faceted, and compounding. Such risks stem from socio-economic and environmental stresses that co-evolve and force tipping points and instabilities. Robust decision-making necessitates extensive analyses and model assessments for insights toward solutions. However, these exercises are consumptive in terms of computational and investigative resources. In practical terms, such exercises cannot be performed extensively—but selectively in terms of priority and scale. Therefore, an efficient analysis platform is needed through which the variety of multi-systems/sector observational and simulated data can be readily incorporated, combined, diagnosed, visualized, and in doing so, identifies “hotspots” of salient compounding threats. In view of this, we have constructed a “triage-based” visualization and data-sharing platform—the System for the Triage of Risks from Environmental and Socio-Economic Stressors (STRESS)—that brings together data across socio-environmental systems, economics, demographics, health, biodiversity, and infrastructure. Through the STRESS website, users can display risk indices that result from weighted combinations of risk metrics they can select. Currently, these risk metrics include land-, water-, and energy systems, biodiversity, as well as demographics, environmental equity, and transportation networks. We highlight the utility of the STRESS platform through several demonstrative analyses over the United States from the national to county level. The STRESS is an open-science tool and available to the community-at-large. We will continue to develop it with an open, accessible, and interactive approach, including academics, researchers, industry, and the general public

    A reduced order modeling methodology for the parametric estimation and optimization of aviation noise

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    The successful mitigation of aviation noise is one of the key enablers of sustainable aviation growth. Technological improvements for noise reduction at the source have been countered by increasing number of operations at most airports. There are several consequences of aviation noise including direct health effects, effects on human and non-human environments, and economic costs. Several mitigation strategies exist including reduction of noise at source, land-use planning and management, noise abatement operational procedures, and operating restrictions. Most noise management programs at airports use a combination of such mitigation measures. To assess the efficacy of noise mitigation measures, a robust modeling and simulation capability is required. Due to the large number of factors which can influence aviation noise metrics, current state-of-the-art tools rely on physics-based and semi-empirical models. These models help in accurately predicting noise metrics in a wide range of scenarios; however, they are computationally expensive to evaluate. Therefore, current noise mitigation studies are limited to singular applications such as annual average day noise quantification. Many-query applications such as parametric trade-off analyses and optimization remain elusive with the current generation of tools and methods. There are several efforts documented in literature which attempt to speed up the process using surrogate models. Techniques include the use of pre-computed noise grids with calibration models for non-standard conditions. These techniques are typically predicated on simplifying assumptions which greatly limit the applicability of such models. Simplifying assumptions are needed to downsize the number influencing factors to be modeled and make the problem tractable. Existing efforts also suffer due to the inclusion of categorical variables for operational profiles which are not conducive to surrogate modeling. In this research, a methodology is developed to address the inherent complexities of the noise quantification process, and thus enable rapid noise modeling capabilities which can facilitate parametric trade-off analysis and optimization efforts. To achieve this objective, a research plan is developed and executed to address two major gaps in literature. First, a parametric representation of operational profiles is proposed to replace existing categorical descriptions. A technique is developed to allow real-world flight data to be efficiently mapped onto this parametric definition. A trajectory clustering method is used to group similar flights and representative flights are parametrized using an inverse-map of an aircraft performance model. Next, a field surrogate modeling method is developed based on Model Order Reduction techniques to reduce the high dimensionality of computed noise metric results. This greatly reduces the complexity of data to be modeled, and thus enables rapid noise quantification. With these two gaps addressed, the overall methodology is developed for rapid noise quantification and optimization. This methodology is demonstrated on a case study where a large number of real-world flight trajectories are efficiently modeled for their noise results. As each such flight trajectory has a unique representation, and typically lacks thrust information, such noise modeling is not computationally feasible with existing methods and tools. The developed parametric representations and field surrogate modeling capabilities enable such an application.Ph.D
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