63 resultados para Airport infrastructure


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The US National Academy of Engineering recently identified restoring and improving urban infrastructure as one of the grand challenges of engineering. Part of this challenge stems from the lack of viable methods to map/label existing infrastructure. For computer vision, this challenge becomes “How can we automate the process of extracting geometric, object oriented models of infrastructure from visual data?” Object recognition and reconstruction methods have been successfully devised and/or adapted to answer this question for small or linear objects (e.g. columns). However, many infrastructure objects are large and/or planar without significant and distinctive features, such as walls, floor slabs, and bridge decks. How can we recognize and reconstruct them in a 3D model? In this paper, strategies for infrastructure object recognition and reconstruction are presented, to set the stage for posing the question above and discuss future research in featureless, large/planar object recognition and modeling.

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Three-dimensional (3-D) spatial data of a transportation infrastructure contain useful information for civil engineering applications, including as-built documentation, on-site safety enhancements, and progress monitoring. Several techniques have been developed for acquiring 3-D point coordinates of infrastructure, such as laser scanning. Although the method yields accurate results, the high device costs and human effort required render the process infeasible for generic applications in the construction industry. A quick and reliable approach, which is based on the principles of stereo vision, is proposed for generating a depth map of an infrastructure. Initially, two images are captured by two similar stereo cameras at the scene of the infrastructure. A Harris feature detector is used to extract feature points from the first view, and an innovative adaptive window-matching technique is used to compute feature point correspondences in the second view. A robust algorithm computes the nonfeature point correspondences. Thus, the correspondences of all the points in the scene are obtained. After all correspondences have been obtained, the geometric principles of stereo vision are used to generate a dense depth map of the scene. The proposed algorithm has been tested on several data sets, and results illustrate its potential for stereo correspondence and depth map generation.

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Advances in the development of computer vision, miniature Micro-Electro-Mechanical Systems (MEMS) and Wireless Sensor Network (WSN) offer intriguing possibilities that can radically alter the paradigms underlying existing methods of condition assessment and monitoring of ageing civil engineering infrastructure. This paper describes some of the outcomes of the European Science Foundation project "Micro-Measurement and Monitoring System for Ageing Underground Infrastructures (Underground M3)". The main aim of the project was to develop a system that uses a tiered approach to monitor the degree and rate of tunnel deterioration. The system comprises of (1) Tier 1: Micro-detection using advances in computer vision and (2) Tier 2: Micro-monitoring and communication using advances in MEMS and WSN. These potentially low-cost technologies will be able to reduce costs associated with end-of-life structures, which is essential to the viability of rehabilitation, repair and reuse. The paper describes the actual deployment and testing of these innovative monitoring tools in tunnels of London Underground, Prague Metro and Barcelona Metro. © 2012 Taylor & Francis Group.

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Construction of geotechnical structures produces various environmental impacts. These include depletion of limited natural resources, generation of wastes and harmful substances during material productions and construction, ineffective usage of energy during processing of raw materials into construction materials, and emissions of unwanted gasses during transportation of materials and usage of equipments. With increasing interests in sustainability at the global scale, there is a need to develop a methodology that can assess environmental impacts at such scale for geotechnical construction. Using embodied energy and gas emission, quantitative measures of environmental impact are evaluated using a case study of a new high speed railway line construction in the UK. Based on the results, the keys to energy savings are (a) to optimise the usage of materials with high embodied energy intensity value (b) to optimise the transportation network and logistics for processes using primarily low embodied energy intensity materials and (c) to reuse as much materials on-site as possible to minimise the quantity of spoils or distance to disposal sites. The evaluated embodied energy and embodied carbon values are compared to those of other types of structures and of other activities and carbon tax values. Such comparisons can be used to discuss among various interested parties (clients, contractors, consultants, policy makers, etc) to make the construction industry more energy efficient. © Springer Science+Business Media B.V. 2011.

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This paper considers how the provision of integrated household-level infrastructure – particularly water and environmental sanitation (including water supply, sewerage, roads, storm drainage and solid waste management) –can play a leading role in improving the conditions in slum settlements. Around 700 socio-economic interviews were carried out in India and South Africa to investigate an innovative approach called slum networking, which sees the strong correlation between slum locations and drainage paths as an opportunity for improving the wider urban environment. This recognition allows resources to be mobilised locally, thereby removing the need for external aid funding. The evidence from the 700 families shows that communities perceive water and sanitation inputs to be their top priority and are willing to contribute to the costs. If slum upgrading is led with access to integrated water and environmental sanitation at household level with community contributions to the cost of infrastructure, then slum communities subsequently invest considerably greater sums in improved housing and education, with longer term contributions to poverty alleviation, improvements in health and literacy and an increase in disposable incomes.