Automated 3D Structure Inference of Civil Infrastructure Using a Stereo Camera Set

The commercial far-range (>10m) infrastructure spatial data collection methods are not completely automated. They need significant amount of manual post-processing work and in some cases, the equipment costs are significant. This paper presents a method that is the first step of a stereo videogrammetric framework and holds the promise to address these issues. Under this method, video streams are initially collected from a calibrated set of two video cameras. For each pair of simultaneous video frames, visual feature points are detected and their spatial coordinates are then computed. The result, in the form of a sparse 3D point cloud, is the basis for the next steps in the framework (i.e., camera motion estimation and dense 3D reconstruction). A set of data, collected from an ongoing infrastructure project, is used to show the merits of the method. Comparison with existing tools is also shown, to indicate the performance differences of the proposed method in the level of automation and the accuracy of results.

Generating the Sparse Point Cloud of a Civil Infrastructure Scene Using a Single Video Camera under Practical Constraints

Automating the model generation process of infrastructure can substantially reduce the modeling time and cost. This paper presents a method to generate a sparse point cloud of an infrastructure scene using a single video camera under practical constraints. It is the first step towards establishing an automatic framework for object-oriented as-built modeling. Motion blur and key frame selection criteria are considered. Structure from motion and bundle adjustment are explored. The method is demonstrated in a case study where the scene of a reinforced concrete bridge is videotaped, reconstructed, and metrically validated. The result indicates the applicability, efficiency, and accuracy of the proposed method.

Optimized selection of key frames for monocular videogrammetric surveying of civil infrastructure

Videogrammetry is an inexpensive and easy-to-use technology for spatial 3D scene recovery. When applied to large scale civil infrastructure scenes, only a small percentage of the collected video frames are required to achieve robust results. However, choosing the right frames requires careful consideration. Videotaping a built infrastructure scene results in large video files filled with blurry, noisy, or redundant frames. This is due to frame rate to camera speed ratios that are often higher than necessary; camera and lens imperfections and limitations that result in imaging noise; and occasional jerky motions of the camera that result in motion blur; all of which can significantly affect the performance of the videogrammetric pipeline. To tackle these issues, this paper proposes a novel method for automating the selection of an optimized number of informative, high quality frames. According to this method, as the first step, blurred frames are removed using the thresholds determined based on a minimum level of frame quality required to obtain robust results. Then, an optimum number of key frames are selected from the remaining frames using the selection criteria devised by the authors. Experimental results show that the proposed method outperforms existing methods in terms of improved 3D reconstruction results, while maintaining the optimum number of extracted frames needed to generate high quality 3D point clouds.© 2012 Elsevier Ltd. All rights reserved.

Achievements and Challenges in Recognizing and Reconstructing Civil Infrastructure

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.

Global civil aviation black carbon emissions.

Aircraft black carbon (BC) emissions contribute to climate forcing, but few estimates of BC emitted by aircraft at cruise exist. For the majority of aircraft engines the only BC-related measurement available is smoke number (SN)-a filter based optical method designed to measure near-ground plume visibility, not mass. While the first order approximation (FOA3) technique has been developed to estimate BC mass emissions normalized by fuel burn [EI(BC)] from SN, it is shown that it underestimates EI(BC) by >90% in 35% of directly measured cases (R(2) = -0.10). As there are no plans to measure BC emissions from all existing certified engines-which will be in service for several decades-it is necessary to estimate EI(BC) for existing aircraft on the ground and at cruise. An alternative method, called FOX, that is independent of the SN is developed to estimate BC emissions. Estimates of EI(BC) at ground level are significantly improved (R(2) = 0.68), whereas estimates at cruise are within 30% of measurements. Implementing this approach for global civil aviation estimated aircraft BC emissions are revised upward by a factor of ~3. Direct radiative forcing (RF) due to aviation BC emissions is estimated to be ~9.5 mW/m(2), equivalent to ~1/3 of the current RF due to aviation CO2 emissions.

A numerical study into the effects of Bio-synthetic Paraffinic Kerosine blends with Jet-A fuel for civil aircraft engine

Growing concerns regarding fluctuating fuel costs and pollution targets for gas emissions, have led the aviation industry to seek alternative technologies to reduce its dependency on crude oil, and its net emissions. Recently blends of bio-fuel with kerosine, have become an alternative solution as they offer "greener" aircraft and reduce demand on crude oil. Interestingly, this technique is able to be implemented in current aircraft as it does not require any modification to the engine. Therefore, the present study investigates the effect of blends of bio-synthetic paraffinic kerosine with Jet-A in a civil aircraft engine, focusing on its performance and exhaust emissions. Two bio-fuels are considered: Jatropha Bio-synthetic Paraffinic Kerosine (JSPK) and Camelina Bio-synthetic Paraffinic Kerosine (CSPK); there are evaluated as pure fuels, and as 10% and 50% blend with Jet-A. Results obtained show improvement in thrust, fuel flow and SFC as composition of bio-fuel in the blend increases. At design point condition, results on engine emissions show reduction in NO x, and CO, but increases of CO is observed at fixed fuel condition, as the composition of bio-fuel in the mixture increases. Copyright © 2012 by ASME.

Risk-based approaches to sustainability in civil engineering

Infrastructure project sustainability assessment typically entails the use of specialised assessment tools to measure and rate project performance against a set of criteria. This paper looks beyond the prevailing approaches to sustainability assessments and explores sustainability principles in terms of project risks and opportunities. Taking a risk management approach to applying sustainability concepts to projects has the potential to reconceptualise decision structures for sustainability from bespoke assessments to becoming a standard part of the project decisionmaking process. By integrating issues of sustainability into project risk management for project planning, design and construction, sustainability is considered within a more traditional business and engineering language. Currently, there is no widely practised approach for objectively considering the environmental and social context of projects alongside the more traditional project risk assessments of time, cost and quality. A risk-based approach would not solve all the issues associated with existing sustainability assessments but it would place sustainability concerns alongside other key risks and opportunities, integrating sustainability with other project decisions.