868 research outputs found

    Dynamic performance of transmission pole structures under blasting induced ground vibration

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    Structural integrity of electric transmission poles is crucial for the reliability of power delivery. In some areas where blasting is used for mining or construction, these structures are endangered if they are located close to blasting sites. Through field study, numerical simulation and theoretical analysis, this research investigates blast induced ground vibration and its effects on structural performance of the transmission poles. It mainly involves: (1) Blast induced ground motion characterization; (2) Determination of modal behavior of transmission poles; (3) Investigation of dynamic responses of transmission poles under blast induced ground excitations; (4) Establishment of a reasonable blast limit for pole structures; and (5) Development of heath monitoring strategies for the electric transmission structures. The main technical contributions of this research include: (1) developed site specific spectra of blast induced ground vibration based on field measurement data; (2) studied modal behavior of pole structures systematically; (3) proposed simplified but relatively accurate finite element (FE) models that consider the structure-cable coupling; (4) obtained dynamic responses of transmission pole structures under blast caused ground vibration both by spectrum and time-history analysis; (5) established 2 in/s PPV blast limit for transmission pole structures; (6) developed two NDT techniques for quality control of direct embedment foundations; and (7) described an idea of vibration-based health monitoring strategy for electric transmission structures schematically

    Interferometric Synthetic Aperture RADAR and Radargrammetry towards the Categorization of Building Changes

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    The purpose of this work is the investigation of SAR techniques relying on multi image acquisition for fully automatic and rapid change detection analysis at building level. In particular, the benefits and limitations of a complementary use of two specific SAR techniques, InSAR and radargrammetry, in an emergency context are examined in term of quickness, globality and accuracy. The analysis is performed using spaceborne SAR data

    Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

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    This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

    Terrestrial LiDAR-based bridge evaluation

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    Considering the over half million bridges in the US state highway system, more than 70% of which were built before 1935, it is of little wonder that bridge maintenance and management is facing severe challenges and the significant funding scarcity rapidly escalates the problem. Commercial remote sensing techniques have the capability of covering large areas and are suggested to be cost effective methods for bridge inspection. This dissertation introduces several applications of the remote bridge inspection technologies using ground-based LiDAR systems. In particular, the application of terrestrial LiDAR for bridge health monitoring is studied. An automatic bridge condition evaluation system based on terrestrial LiDAR data, LiBE (LiDAR-based Bridge Evaluation), is developed. The research works completed thus far have shown that LiDAR technology has the capability for bridge surface defect detection and quantification, clearance measurement, and displacement measurement during bridge static load testing. Several bridges in Mecklenburg County, NC, and other areas have been evaluated using LiBE and quantitative bridge rating mechanisms are proposed. A cost-benefit analysis has been conducted that demonstrates the relevancy of the technique to current nation-wide bridge management problem, as well as, the potential of reducing the bridge maintenance costs to the stack holders. The results generated from these technologies are valuable for bridge maintenance decision making

    Computing fast search heuristics for physics-based mobile robot motion planning

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    Mobile robots are increasingly being employed to assist responders in search and rescue missions. Robots have to navigate in dangerous areas such as collapsed buildings and hazardous sites, which can be inaccessible to humans. Tele-operating the robots can be stressing for the human operators, which are also overloaded with mission tasks and coordination overhead, so it is important to provide the robot with some degree of autonomy, to lighten up the task for the human operator and also to ensure robot safety. Moving robots around requires reasoning, including interpretation of the environment, spatial reasoning, planning of actions (motion), and execution. This is particularly challenging when the environment is unstructured, and the terrain is \textit{harsh}, i.e. not flat and cluttered with obstacles. Approaches reducing the problem to a 2D path planning problem fall short, and many of those who reason about the problem in 3D don't do it in a complete and exhaustive manner. The approach proposed in this thesis is to use rigid body simulation to obtain a more truthful model of the reality, i.e. of the interaction between the robot and the environment. Such a simulation obeys the laws of physics, takes into account the geometry of the environment, the geometry of the robot, and any dynamic constraints that may be in place. The physics-based motion planning approach by itself is also highly intractable due to the computational load required to perform state propagation combined with the exponential blowup of planning; additionally, there are more technical limitations that disallow us to use things such as state sampling or state steering, which are known to be effective in solving the problem in simpler domains. The proposed solution to this problem is to compute heuristics that can bias the search towards the goal, so as to quickly converge towards the solution. With such a model, the search space is a rich space, which can only contain states which are physically reachable by the robot, and also tells us enough information about the safety of the robot itself. The overall result is that by using this framework the robot engineer has a simpler job of encoding the \textit{domain knowledge} which now consists only of providing the robot geometric model plus any constraints

    Automated Building Information Extraction and Evaluation from High-resolution Remotely Sensed Data

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    The two-dimensional (2D) footprints and three-dimensional (3D) structures of buildings are of great importance to city planning, natural disaster management, and virtual environmental simulation. As traditional manual methodologies for collecting 2D and 3D building information are often both time consuming and costly, automated methods are required for efficient large area mapping. It is challenging to extract building information from remotely sensed data, considering the complex nature of urban environments and their associated intricate building structures. Most 2D evaluation methods are focused on classification accuracy, while other dimensions of extraction accuracy are ignored. To assess 2D building extraction methods, a multi-criteria evaluation system has been designed. The proposed system consists of matched rate, shape similarity, and positional accuracy. Experimentation with four methods demonstrates that the proposed multi-criteria system is more comprehensive and effective, in comparison with traditional accuracy assessment metrics. Building height is critical for building 3D structure extraction. As data sources for height estimation, digital surface models (DSMs) that are derived from stereo images using existing software typically provide low accuracy results in terms of rooftop elevations. Therefore, a new image matching method is proposed by adding building footprint maps as constraints. Validation demonstrates that the proposed matching method can estimate building rooftop elevation with one third of the error encountered when using current commercial software. With an ideal input DSM, building height can be estimated by the elevation contrast inside and outside a building footprint. However, occlusions and shadows cause indistinct building edges in the DSMs generated from stereo images. Therefore, a “building-ground elevation difference model” (EDM) has been designed, which describes the trend of the elevation difference between a building and its neighbours, in order to find elevation values at bare ground. Experiments using this novel approach report that estimated building height with 1.5m residual, which out-performs conventional filtering methods. Finally, 3D buildings are digitally reconstructed and evaluated. Current 3D evaluation methods did not present the difference between 2D and 3D evaluation methods well; traditionally, wall accuracy is ignored. To address these problems, this thesis designs an evaluation system with three components: volume, surface, and point. As such, the resultant multi-criteria system provides an improved evaluation method for building reconstruction

    Investigation of Computer Vision Concepts and Methods for Structural Health Monitoring and Identification Applications

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    This study presents a comprehensive investigation of methods and technologies for developing a computer vision-based framework for Structural Health Monitoring (SHM) and Structural Identification (St-Id) for civil infrastructure systems, with particular emphasis on various types of bridges. SHM is implemented on various structures over the last two decades, yet, there are some issues such as considerable cost, field implementation time and excessive labor needs for the instrumentation of sensors, cable wiring work and possible interruptions during implementation. These issues make it only viable when major investments for SHM are warranted for decision making. For other cases, there needs to be a practical and effective solution, which computer-vision based framework can be a viable alternative. Computer vision based SHM has been explored over the last decade. Unlike most of the vision-based structural identification studies and practices, which focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation, the proposed framework combines the vision-based structural input and the structural output from non-contact sensors to overcome the limitations given above. First, this study develops a series of computer vision-based displacement measurement methods for structural response (structural output) monitoring which can be applied to different infrastructures such as grandstands, stadiums, towers, footbridges, small/medium span concrete bridges, railway bridges, and long span bridges, and under different loading cases such as human crowd, pedestrians, wind, vehicle, etc. Structural behavior, modal properties, load carrying capacities, structural serviceability and performance are investigated using vision-based methods and validated by comparing with conventional SHM approaches. In this study, some of the most famous landmark structures such as long span bridges are utilized as case studies. This study also investigated the serviceability status of structures by using computer vision-based methods. Subsequently, issues and considerations for computer vision-based measurement in field application are discussed and recommendations are provided for better results. This study also proposes a robust vision-based method for displacement measurement using spatio-temporal context learning and Taylor approximation to overcome the difficulties of vision-based monitoring under adverse environmental factors such as fog and illumination change. In addition, it is shown that the external load distribution on structures (structural input) can be estimated by using visual tracking, and afterward load rating of a bridge can be determined by using the load distribution factors extracted from computer vision-based methods. By combining the structural input and output results, the unit influence line (UIL) of structures are extracted during daily traffic just using cameras from which the external loads can be estimated by using just cameras and extracted UIL. Finally, the condition assessment at global structural level can be achieved using the structural input and output, both obtained from computer vision approaches, would give a normalized response irrespective of the type and/or load configurations of the vehicles or human loads

    Fracture network characterization in enhanced geothermal systems by induced seismicity analysis

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    Subject of the doctoral project is to study induced seismicity in enhanced geothermal systems to characterize the underground fracture network. The first part of this work focuses on the case study of the Rittershoffen deep geothermal reservoir. It is demonstrated how the integration of advanced processing techniques can lead to a deeper insight into the structure of the fault system and its reaction to repeated fluid injection. In the second part of this work, a new method is proposed to highlight the fracture network in seismic clouds that do not form apparent planar structures. With this method, the likelihood of having a fracture at a given location is computed from the distribution of seismic events and their source parameters

    High-performance tsunami modelling with modern GPU technology

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    PhD ThesisEarthquake-induced tsunamis commonly propagate in the deep ocean as long waves and develop into sharp-fronted surges moving rapidly coastward, which may be effectively simulated by hydrodynamic models solving the nonlinear shallow water equations (SWEs). Tsunamis can cause substantial economic and human losses, which could be mitigated through early warning systems given efficient and accurate modelling. Most existing tsunami models require long simulation times for real-world applications. This thesis presents a graphics processing unit (GPU) accelerated finite volume hydrodynamic model using the compute unified device architecture (CUDA) for computationally efficient tsunami simulations. Compared with a standard PC, the model is able to reduce run-time by a factor of > 40. The validated model is used to reproduce the 2011 Japan tsunami. Two source models were tested, one based on tsunami waveform inversion and another using deep-ocean tsunameters. Vertical sea surface displacement is computed by the Okada model, assuming instantaneous sea-floor deformation. Both source models can reproduce the wave propagation at offshore and nearshore gauges, but the tsunameter-based model better simulates the first wave amplitude. Effects of grid resolutions between 450-3600 m, slope limiters, and numerical accuracy are also investigated for the simulation of the 2011 Japan tsunami. Grid resolutions of 1-2 km perform well with a proper limiter; the Sweby limiter is optimal for coarser resolutions, recovers wave peaks better than minmod, and is more numerically stable than Superbee. One hour of tsunami propagation can be predicted in 50 times on a regular low-cost PC-hosted GPU, compared to a single CPU. For 450 m resolution on a larger-memory server-hosted GPU, performance increased by ~70 times. Finally, two adaptive mesh refinement (AMR) techniques including simplified dynamic adaptive grids on CPU and a static adaptive grid on GPU are introduced to provide multi-scale simulations. Both can reduce run-time by ~3 times while maintaining acceptable accuracy. The proposed computationally-efficient tsunami model is expected to provide a new practical tool for tsunami modelling for different purposes, including real-time warning, evacuation planning, risk management and city planning
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