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Laser-Based Structural Sensing and Surface Damage Detection

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Laser-Based Structural Sensing and Surface Damage Detection DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING REPORTS Laser-Based Structural Sensing and Surface Damage Detection September 2014 Burcu Guldur Northeastern University Jerome F. Hajjar Northeastern University Report No. NEU-CEE-2014-03 Recommended Citation Guldur, Burcu and Hajjar, Jerome F., "Laser-Based Structural Sensing and Surface Damage Detection" (2014). Department of Civil and Environmental Engineering Reports. Report No. NEU-CEE-2014-03. Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts. This report is available open access, hosted by Northeastern University. Northeastern University was founded in 1898, as a private research university. Northeastern University is a leader in worldwide experiential learning, urban engagement, and interdisciplinary research that meets global and societal needs. Department of Civil and Environmental Engineering has over 100 years of history and tradition in research, teaching and service to the community, making important contributions to the development of our civil infrastructure and the environment, both nationally and internationally. Contact: Department of Civil & Environmental Engineering 400 Snell Engineering Center Northeastern University 360 Huntington Avenue Boston, MA 02115 (617) 373-2444 (617) 373-4419 (fax) The authors thank M. L. Wang, D. P. Bernal, M. MacNeil, M. Clifford at DGT Survey Group, and Faro Technologies for their contributions to this research. This material is based upon work supported by the National Science Foundation under Grant No. IIS-1328816 and Northeastern University. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Northeastern University Boston, Massachusetts September 2014 Abstract Damage due to age or accumulated damage from hazards on existing structures poses a worldwide problem. In order to evaluate the current status of aging, deteriorating and damaged structures, it is vital to accurately assess the present conditions. It is possible to capture the in situ condition of structures by using laser scanners that create dense three-dimensional point clouds. This research investigates the use of high resolution three-dimensional terrestrial laser scanners with image capturing abilities as tools to capture geometric range data of complex scenes for structural engineering applications. Laser scanning technology is continuously improving, with commonly available scanners now capturing over 1,000,000 texture-mapped points per second with an accuracy of ~2 mm. However, automatically extracting meaningful information from point clouds remains a challenge, and the current state-of-the-art requires significant user interaction. The first objective of this research is to use widely accepted point cloud processing steps such as registration, feature extraction, segmentation, surface fitting and object detection to divide laser scanner data into meaningful object clusters and then apply several damage detection methods to these clusters. This required establishing a process for extracting important information from raw laser-scanned data sets such as the location, orientation and size of objects in a scanned region, and location of damaged regions on a structure. For this purpose, first a methodology for processing range data to identify objects in a scene is presented and then, once the objects from model library are correctly detected and fitted into the captured point cloud, these fitted objects are compared with the as-is point cloud of the investigated object to locate defects on the structure. The algorithms are demonstrated on synthetic scenes and validated on range i data collected from test specimens and test-bed bridges. The second objective of this research is to combine useful information extracted from laser scanner data with color information, which provides information in the fourth dimension that enables detection of damage types such as cracks, corrosion, and related surface defects that are generally difficult to detect using only laser scanner data; moreover, the color information also helps to track volumetric changes on structures such as spalling. Although using images with varying resolution to detect cracks is an extensively researched topic, damage detection using laser scanners with and without color images is a new research area that holds many opportunities for enhancing the current practice of visual inspections. The aim is to combine the best features of laser scans and images to create an automatic and effective surface damage detection method, which will reduce the need for skilled labor during visual inspections and allow automatic documentation of related information. This work enables developing surface damage detection strategies that integrate existing condition rating criteria for a wide range damage types that are collected under three main categories: small deformations already existing on the structure (cracks); damage types that induce larger deformations, but where the initial topology of the structure has not changed appreciably (e.g., bent members); and large deformations where localized changes in the topology of the structure have occurred (e.g., rupture, discontinuities and spalling). The effectiveness of the developed damage detection algorithms are validated by comparing the detection results with the measurements taken from test specimens and test-bed bridges. ii Table of Contents Abstract ............................................................................................................................... i Acknowledgements .............................................................. Error! Bookmark not defined. Table of Contents ............................................................................................................. iii Figures ............................................................................................................................. viii Tables .............................................................................................................................. xiv List of the Common Terms ......................................................................................... xviii 1. Introduction ................................................................................................................... 1 1.1 Objectives and Scope .................................................................................... 6 1.1.1 Small Deformations ............................................................................... 6 1.1.2 Large Deformations with No Change in Topology ............................... 7 1.1.3 Large Deformations with Localized Change in Topology .................... 7 1.2 Organization ................................................................................................ 10 2. Background ................................................................................................................. 13 2.1 3D Laser Scanners ...................................................................................... 14 2.2 Key Specifications and Sources of Error for Time-of-flight and Triangulation-based 3D Laser Scanners ................................................................... 18 2.2.1 Key Specifications ............................................................................... 18 2.2.2 Sources of Errors ................................................................................. 19 2.2.3 Incomplete data .................................................................................... 21 2.3 Laser Scanning and Image Processing Applications in Civil Engineering. 22 2.4 Laser Scan Processing for Modeling and Damage Detection ..................... 22 2.4.1 Registration .......................................................................................... 23 2.4.2 Feature Detection ................................................................................. 24 2.4.3 Segmentation ....................................................................................... 25 2.4.3.1 Region Growing for Segmentation ................................................. 26 2.4.4 Object Detection .................................................................................. 27 2.5 Laser-based Modeling ................................................................................. 30 2.6 Laser-based Damage Detection .................................................................. 31 2.7 Image Processing for Damage Detection .................................................... 36 2.8 Combined Laser and Image Processing for Damage Detection ................. 38 2.9 Current Inspection Strategies for Bridges ................................................... 39 3. Research Methodology ............................................................................................... 43 iii 3.1 Summary of Detectable Damage Types ..................................................... 43 3.2 Research Strategies ..................................................................................... 46 3.2.1 Methods for Small Deformations ........................................................ 47 3.2.2 Methods for Large Deformations with No Change in Topology ........ 48 3.2.3 Methods for Large Deformations with Localized Change in Topology . ............................................................................................................
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