Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

[1]  Qian Wang,et al.  DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection , 2019, IEEE Transactions on Image Processing.

[2]  A. E. Cawkell The investigation of the quality of thick concrete by ultrasonic pulse propagation , 1958 .

[3]  P. Falorni,et al.  The Estimation of Buried Pipe Diameters by Generalized Hough Transform of Radar Data , 2005 .

[4]  Qingquan Li,et al.  An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection , 2017, Image Vis. Comput..

[5]  Mark A. Minor,et al.  Design, implementation, and evaluation of an under-actuated miniature biped climbing robot , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[6]  A. E. Cawkell Discussion: The investigation of the quality of thick concrete by ultrasonic pulse propagation* , 1958 .

[7]  Aditya Roshan,et al.  Understanding the Quality of Pansharpening – A Lab Study , 2016 .

[8]  P. Cawley,et al.  Data fusion for automated non-destructive inspection , 2014, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[9]  Bo Dai,et al.  Improved online sequential extreme learning machine for identifying crack behavior in concrete dam , 2018, Advances in Structural Engineering.

[10]  John Vantomme,et al.  Detection and evaluation of cracks in the concrete buffer of the Belgian Nuclear Waste container using combined NDT techniques , 2015 .

[11]  Hyoungkwan Kim,et al.  Encoder–decoder network for pixel‐level road crack detection in black‐box images , 2019, Comput. Aided Civ. Infrastructure Eng..

[12]  Lars C. T. Overgaard,et al.  Automated counting of off-axis tunnelling cracks using digital image processing , 2016 .

[13]  Kyeong Ho Cho,et al.  Caterpillar-based cable climbing robot for inspection of suspension bridge hanger rope , 2013, 2013 IEEE International Conference on Automation Science and Engineering (CASE).

[14]  Yang Liu,et al.  Deep Learning-Based Fully Automated Pavement Crack Detection on 3D Asphalt Surfaces with an Improved CrackNet , 2018, J. Comput. Civ. Eng..

[15]  Yong Liu,et al.  An approach for auto bridge inspection based on climbing robot , 2013, 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[16]  Stephen Pierce,et al.  Data fusion in automated robotic inspection systems , 2008 .

[17]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[18]  Dinesh Rajan,et al.  Concrete crack detection using context‐aware deep semantic segmentation network , 2019, Comput. Aided Civ. Infrastructure Eng..

[19]  John S. Popovics,et al.  NDE techniques for concrete and masonry structures , 2003 .

[20]  X. Lucas Travassos,et al.  Ground Penetrating Radar , 2008 .

[21]  Parisa Shokouhi,et al.  Application of data fusion in nondestructive testing (NDT) , 2013, Proceedings of the 16th International Conference on Information Fusion.

[22]  S. Shihab,et al.  Radius Estimation for Cylindrical Objects Detected by Ground Penetrating Radar , 2005 .

[23]  Farid Melgani,et al.  Automatic Analysis of GPR Images: A Pattern-Recognition Approach , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Hung Manh La,et al.  A Genetic Algorithm for Convolutional Network Structure Optimization for Concrete Crack Detection , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[25]  Vineet R. Kamat,et al.  GPR Signature Detection and Decomposition for Mapping Buried Utilities with Complex Spatial Configuration , 2018, J. Comput. Civ. Eng..

[26]  Steve Millard,et al.  Location of steel reinforcement in concrete using ground penetrating radar and neural networks , 2005 .

[27]  Paolo Gamba,et al.  A fuzzy shell clustering approach to recognize hyperbolic signatures in subsurface radar images , 2000, IEEE Trans. Geosci. Remote. Sens..

[28]  John S. Popovics,et al.  Nondestructive Bridge Deck Testing with Air-Coupled Impact-Echo and Infrared Thermography , 2012 .

[29]  Saleh Abu Dabous,et al.  Condition monitoring of bridges with non-contact testing technologies , 2020 .

[30]  Takahide Sakagami Remote nondestructive evaluation technique using infrared thermography for fatigue cracks in steel bridges , 2015 .

[31]  Robin R. Murphy,et al.  Robot-Assisted Bridge Inspection , 2011, J. Intell. Robotic Syst..

[32]  Jinying Zhu,et al.  Imaging Concrete Structures Using Air-Coupled Impact-Echo , 2007 .

[33]  Mohamed Al-Hussein,et al.  A scientometric analysis and critical review of computer vision applications for construction , 2019, Automation in Construction.

[34]  Ning Ding,et al.  Mechanical Design of a Cable Climbing Robot for Inspection on a Cable-Stayed Bridge , 2018, 2018 13th World Congress on Intelligent Control and Automation (WCICA).

[35]  Marie-Aude Ploix,et al.  NDE data fusion to improve the evaluation of concrete structures , 2011 .

[36]  Raffaele Persico,et al.  Automated Detection of Reflection Hyperbolas in Complex GPR Images With No A Priori Knowledge on the Medium , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Sofiane Amziane,et al.  Flexural cracking behavior of normal strength, high strength and high strength fiber concrete beams, using Digital Image Correlation technique , 2016 .

[38]  Paul Fieguth,et al.  Automated detection of cracks in buried concrete pipe images , 2006 .

[39]  Anand K. Gramopadhye,et al.  A survey of automation-enabled human-in-the-loop systems for infrastructure visual inspection , 2019, Automation in Construction.

[40]  Bidyut Baran Chaudhuri,et al.  Elliptic fit of objects in two and three dimensions by moment of inertia optimization , 1991, Pattern Recognit. Lett..

[41]  Hung Manh La,et al.  Automated Rebar Detection for Ground-Penetrating Radar , 2016, ISVC.

[42]  Tran Hiep Dinh,et al.  Computer vision-based method for concrete crack detection , 2016, 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV).

[43]  Suyun Ham,et al.  Acoustic evaluation of concrete delaminations using ball-chain impact excitation. , 2017, The Journal of the Acoustical Society of America.

[44]  Koichi Kobayashi,et al.  Corrosion detection in reinforced concrete using induction heating and infrared thermography , 2011 .

[45]  Colin G. Windsor,et al.  A Data Pair-Labeled Generalized Hough Transform for Radar Location of Buried Objects , 2014, IEEE Geoscience and Remote Sensing Letters.

[46]  Ezzatollah Salari,et al.  Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification , 2010, Comput. Aided Civ. Infrastructure Eng..

[47]  Qian Chen,et al.  Construction automation: Research areas, industry concerns and suggestions for advancement , 2018, Automation in Construction.

[48]  Soheil Nazarian,et al.  Nondestructive testing to identify concrete bridge deck deterioration , 2012 .

[49]  Hung Manh La,et al.  Visual and 3D Mapping for Steel Bridge Inspection Using a Climbing Robot , 2016 .

[50]  C. H. Kuo,et al.  Thrust Vectoring Control for Infrastructure Inspection Multirotor Vehicle , 2019, 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA).

[51]  Ahmed Loukili,et al.  Use of the digital image correlation and acoustic emission technique to study the effect of structural size on cracking of reinforced concrete , 2015 .

[52]  Jörg Schmalzl,et al.  Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar , 2013, Comput. Geosci..

[53]  Paul Chinowsky,et al.  Estimated effects of climate change on flood vulnerability of U.S. bridges , 2012, Mitigation and Adaptation Strategies for Global Change.

[54]  Hassan Masoom,et al.  Target detection in diagnostic ultrasound: Evaluation of a method based on the CLEAN algorithm. , 2013, Ultrasonics.

[55]  Eugene Usdin,et al.  Dispersive properties of stratified elastic and liquid media; a ray theory , 1953 .

[56]  Christian A. Mueller,et al.  Robotic bridge inspection within strategic flood evacuation planning , 2017, OCEANS 2017 - Aberdeen.

[57]  Andrew S. Whittaker,et al.  AUTOMATED DETECTION AND MEASUREMENT OF CRACKS IN REINFORCED CONCRETE COMPONENTS , 2015 .

[58]  T. Rakha,et al.  Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones , 2018, Automation in Construction.

[59]  Wei Song,et al.  Deep learning-based roadway crack classification using laser-scanned range images: A comparative study on hyperparameter selection , 2020 .

[60]  Weihua Sheng,et al.  Developing a crack inspection robot for bridge maintenance , 2011, 2011 IEEE International Conference on Robotics and Automation.

[61]  Ting Yuan,et al.  Track level fusion of extended objects from heterogeneous sensors , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[62]  J. Hugenschmidt,et al.  Concrete bridge inspection with a mobile GPR system , 2002 .

[63]  Jian Yao,et al.  Automatic multi-image stitching for concrete bridge inspection by combining point and line features , 2018, Automation in Construction.

[64]  D. Civco,et al.  Road Extraction Using SVM and Image Segmentation , 2004 .

[65]  Stuart J. C. Irvine,et al.  IR Reflectance Imaging for Crystalline Si Solar Cell Crack Detection , 2015, IEEE Journal of Photovoltaics.

[66]  Chao Gao,et al.  Cable surface damage detection in cable-stayed bridges using optical techniques and image mosaicking , 2019 .

[67]  Sami F. Masri,et al.  Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures , 2012 .

[68]  Chih-Hung Chiang,et al.  Defect detection of concrete structures using both infrared thermography and elastic waves , 2008 .

[69]  Xue-jun Xu,et al.  Crack detection of reinforced concrete bridge using video image , 2013 .

[70]  Kristin J. Dana,et al.  Automated Crack Detection on Concrete Bridges , 2016, IEEE Transactions on Automation Science and Engineering.

[71]  Pingrang Wang,et al.  Comparison analysis on present image-based crack detection methods in concrete structures , 2010, 2010 3rd International Congress on Image and Signal Processing.

[72]  Sarah L. Billington,et al.  Historical Analysis of Hydraulic Bridge Collapses in the Continental United States , 2017 .

[73]  Zainah Ibrahim,et al.  Nondestructive test methods for concrete bridges: A review , 2016 .

[74]  Glenn Washer,et al.  Effects of Environmental Variables on Infrared Imaging of Subsurface Features of Concrete Bridges , 2009 .

[75]  Tatsuo Arai,et al.  New tripod walking method for legged inspection robot , 2016, 2016 IEEE International Conference on Mechatronics and Automation.

[76]  Carlos Balaguer,et al.  ROMA robots for inspection of steel based infrastructures , 2002 .

[77]  Leslie M. Collins,et al.  Texture Features for Antitank Landmine Detection Using Ground Penetrating Radar , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[78]  G. B. Wilson Some aspects of data fusion , 1987 .

[79]  Seung-Hyun Eem,et al.  Concrete crack detection and quantification using deep learning and structured light , 2020 .

[80]  Hung Manh La,et al.  A multi-functional inspection robot for civil infrastructure evaluation and maintenance , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[81]  Vladimir P. Vavilov,et al.  Crack detection in aluminum parts by using ultrasound-excited infrared thermography , 2013 .

[82]  Kristin J. Dana,et al.  Automated GPR Rebar Analysis for Robotic Bridge Deck Evaluation , 2016, IEEE Transactions on Cybernetics.

[83]  Lance E. Besaw,et al.  Deep convolutional neural networks for classifying GPR B-scans , 2015, Defense + Security Symposium.

[84]  Mohd Abdullah,et al.  Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique , 2014, EURASIP Journal on Image and Video Processing.

[85]  Jeong Ho Lee,et al.  Bridge inspection robot system with machine vision , 2009 .

[86]  F. Ansari,et al.  Non-destructive testing and evaluation (NDT/NDE) of civil structures rehabilitated using fiber reinforced polymer (FRP) composites , 2011 .

[87]  M. McHugh Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[88]  Paolo Gamba,et al.  Neural detection of pipe signatures in ground penetrating radar images , 2000, IEEE Trans. Geosci. Remote. Sens..

[89]  G. Heredia,et al.  Multirotor UAS for bridge inspection by contact using the ceiling effect , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[90]  Patrik Broberg,et al.  Surface crack detection in welds using thermography , 2013 .

[91]  Chongsheng Cheng,et al.  Time-Series Based Thermography on Concrete Block Void Detection , 2018 .

[92]  Alireza Tavakkoli,et al.  Control Framework for a Hybrid-steel Bridge Inspection Robot , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[93]  Gang Li,et al.  Long-distance precision inspection method for bridge cracks with image processing , 2014 .

[94]  Hung Manh La,et al.  Automated robotic monitoring and inspection of steel structures and bridges , 2017, Robotica.

[95]  Jens Wöstmann,et al.  Evaluation of Radar and Complementary Echo Methods for NDT of Concrete Elements , 2008 .

[96]  Ikhlas Abdel-Qader,et al.  ANALYSIS OF EDGE-DETECTION TECHNIQUES FOR CRACK IDENTIFICATION IN BRIDGES , 2003 .

[97]  G. Muscato,et al.  The Alicia/sup 3/ climbing robot: a three-module robot for automatic wall inspection , 2006, IEEE Robotics & Automation Magazine.

[98]  Jinying Zhu,et al.  Non-Contact NDT of Concrete Structures Using Air -Coupled Sensors , 2006 .

[99]  Siddhartha Kumar Khaitan,et al.  Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection , 2017 .

[100]  Yoshihiko Hamamoto,et al.  A robust automatic crack detection method from noisy concrete surfaces , 2011, Machine Vision and Applications.

[101]  Anibal Ollero,et al.  Contact-Based Bridge Inspection Multirotors: Design, Modeling, and Control Considering the Ceiling Effect , 2019, IEEE Robotics and Automation Letters.

[102]  Wallace T McKeel,et al.  DETECTION OF DELAMINATION IN BRIDGE DECKS WITH INFRARED THERMOGRAPHY , 1978 .

[103]  Ikhlas Abdel-Qader,et al.  Detection of Common Defects in Concrete Bridge Decks Using Nondestructive Evaluation Techniques , 2007 .

[104]  Tatsuo Arai,et al.  A hybrid flying and walking robot for steel bridge inspection , 2016, 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[105]  Bachir Boudraa,et al.  Mathematical morphology for TOFD image analysis and automatic crack detection. , 2014, Ultrasonics.

[106]  Kazuhiro Shimonomura,et al.  High precision marker based localization and movement on the ceiling employing an aerial robot with top mounted omni wheel drive system , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[107]  Shinji Doki,et al.  Measuring position determination for accurate and efficient visual inspection using UAV , 2017, 2017 IEEE/SICE International Symposium on System Integration (SII).

[108]  Giovanni Battista Barla,et al.  3D Laser scanner and thermography for tunnel discontinuity mapping , 2016 .

[109]  Akihiko Ichikawa,et al.  UAV with manipulator for bridge inspection — Hammering system for mounting to UAV , 2017, 2017 IEEE/SICE International Symposium on System Integration (SII).

[110]  Jingang Yi,et al.  RABIT: implementation, performance validation and integration with other robotic platforms for improved management of bridge decks , 2017, International Journal of Intelligent Robotics and Applications.

[111]  Ken'ichi Kawaguchi,et al.  Damaged ceiling detection and localization in large-span structures using convolutional neural networks , 2020 .

[112]  Yukinori Kobayashi,et al.  Mapping of pier substructure using UAV , 2016, 2016 IEEE/SICE International Symposium on System Integration (SII).

[113]  J. Braga,et al.  Aerial manipulator for structure inspection by contact from the underside , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[114]  Kristin J. Dana,et al.  Development of an autonomous bridge deck inspection robotic system , 2017, J. Field Robotics.

[115]  Ja Choon Koo,et al.  Inspection Robot for Hanger Cable of Suspension Bridge: Mechanism Design and Analysis , 2013, IEEE/ASME Transactions on Mechatronics.

[116]  Kyeong Ho Cho,et al.  Development of novel multifunctional robotic crawler for inspection of hanger cables in suspension bridges , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[117]  Tsili Wang,et al.  GPR imaging using the generalized Radon transform , 2000 .

[118]  S. Hirose,et al.  Machine that can walk and climb on floors, walls and ceilings , 1991, Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments.

[119]  Anh Q. Pham,et al.  A Practical Climbing Robot for Steel Bridge Inspection , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[120]  Hyun Myung,et al.  Toward Autonomous Bridge Inspection: A framework and experimental results , 2019, 2019 16th International Conference on Ubiquitous Robots (UR).

[121]  Maria J Grant,et al.  A typology of reviews: an analysis of 14 review types and associated methodologies. , 2009, Health information and libraries journal.

[122]  Jung-Ryul Lee,et al.  A Fully Non-Contact Ultrasonic Propagation Imaging System for Closed Surface Crack Evaluation , 2012 .

[123]  Jingang Yi,et al.  Autonomous robotic system for bridge deck data collection and analysis , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[124]  Jingang Yi,et al.  Mechatronic Systems Design for an Autonomous Robotic System for High-Efficiency Bridge Deck Inspection and Evaluation , 2013, IEEE/ASME Transactions on Mechatronics.

[125]  Bing Lam Luk,et al.  Intelligent legged climbing service robot for remote maintenance applications in hazardous environments , 2005, Robotics Auton. Syst..

[126]  Shahid Kabir Imaging-based detection of AAR induced map-crack damage in concrete structure , 2010 .

[127]  Carlos Balaguer,et al.  Tunnel structural inspection and assessment using an autonomous robotic system , 2018 .

[128]  Hung Manh La,et al.  Autonomous robotic system using non-destructive evaluation methods for bridge deck inspection , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[129]  Tatsuo Arai,et al.  Hammering sound analysis for infrastructure inspection by leg robot , 2015, 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[130]  Francesco Ciampa,et al.  Recent Advances in Active Infrared Thermography for Non-Destructive Testing of Aerospace Components , 2018, Sensors.

[131]  J. E. DeVault,et al.  Robotic system for underwater inspection of bridge piers , 2000 .

[132]  A. Benedetto,et al.  Bridge deck survey with high resolution Ground Penetrating Radar , 2012, 2012 14th International Conference on Ground Penetrating Radar (GPR).

[133]  Jie Gao,et al.  Convolutional Neural Network for Asphalt Pavement Surface Texture Analysis , 2018, Comput. Aided Civ. Infrastructure Eng..

[134]  Xuan Feng,et al.  Subsurface polarimetric migration imaging for full polarimetric ground-penetrating radar , 2015 .

[135]  Ning Ding,et al.  A Light-Weight Wheel-Based Cable Inspection Climbing Robot: From Simulation to Reality , 2018, 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[136]  C. T. Dotter,et al.  NonDestructive Testing , 2008 .

[137]  Nectaria Diamanti,et al.  Field observations and numerical models of GPR response from vertical pavement cracks , 2012 .

[138]  Gang Li,et al.  Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine , 2017 .

[139]  Anthony G. Cohn,et al.  Real-Time Hyperbola Recognition and Fitting in GPR Data , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[140]  H. La,et al.  Rebar Detection using Ground Penetrating Radar with State-ofthe-art Convolutional Neural Networks , 2019 .

[141]  Serena Matucci,et al.  The Detection of Buried Pipes From Time-of-Flight Radar Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[142]  Hung Manh La,et al.  Development of a Steel Bridge Climbing Robot , 2018, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[143]  Oral Büyüköztürk,et al.  Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types , 2018, Comput. Aided Civ. Infrastructure Eng..

[144]  Ivan Bartoli,et al.  Bridge deck delamination identification from unmanned aerial vehicle infrared imagery , 2016 .

[145]  Ning Ding,et al.  Design and Implementation of CCRobot-II: a Palm-based Cable Climbing Robot for Cable-stayed Bridge Inspection , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[146]  Cao Vu Dung,et al.  Autonomous concrete crack detection using deep fully convolutional neural network , 2019, Automation in Construction.

[147]  Alireza Tavakkoli,et al.  Classification of Concrete Crack using Deep Residual Network , 2019 .

[148]  V. M. Malhotra,et al.  CRC Handbook on Nondestructive Testing of Concrete , 1990 .

[149]  Jordan M. Malof,et al.  Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery , 2018, ArXiv.

[150]  Akihiko Ichikawa,et al.  Wall contact by octo-rotor UAV with one DoF manipulator for bridge inspection , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[151]  Ralf W. Arndt,et al.  Comparison of NDT Methods for Assessment of a Concrete Bridge Deck , 2013 .

[152]  Ja Choon Koo,et al.  Multifunctional Robotic Crawler for Inspection of Suspension Bridge Hanger Cables: Mechanism Design and Performance Validation , 2017, IEEE/ASME Transactions on Mechatronics.

[153]  Carlos Ubide,et al.  Analysis of SEM digital images to quantify crack network pattern area in chromium electrodeposits , 2016 .

[154]  Maria Q. Feng,et al.  Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review , 2018 .

[155]  Dewei Meng,et al.  Reducing Thermal Reflections for Infrared Thermography Applications on Tunnel Liners with Reflective Finishes , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[156]  Yong Liu,et al.  Adhesion-adaptive control of a novel bridge-climbing robot , 2013, 2013 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems.

[157]  Junwon Seo,et al.  Drone-enabled bridge inspection methodology and application , 2018, Automation in Construction.

[158]  Nicholas Morozovsky,et al.  SkySweeper: A low DOF, dynamic high wire robot , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[159]  Austin Kovacs,et al.  Detection of Moisture in Construction Materials , 1977 .

[160]  Jung-Wuk Hong,et al.  A vision-based approach for autonomous crack width measurement with flexible kernel , 2020 .

[161]  Mansor Nakhkash,et al.  Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition , 2000 .

[162]  Hung Manh La,et al.  A Magnetic Wheeled Robot for Steel Bridge Inspection , 2019 .

[163]  Kazuhiko Kawamura,et al.  A Rubbertuator-based structure-climbing inspection robot , 1997, Proceedings of International Conference on Robotics and Automation.

[164]  Matthew J. Higgins,et al.  Impacts of Climate Change on Scour-Vulnerable Bridges: Assessment Based on HYRISK , 2013 .

[165]  Wei Lu,et al.  Image-based concrete crack detection in tunnels using deep fully convolutional networks , 2020 .

[166]  Leslie M. Collins,et al.  Histograms of Oriented Gradients for Landmine Detection in Ground-Penetrating Radar Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[167]  Nenad Gucunski,et al.  An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks , 2018 .

[168]  Kaige Zhang,et al.  Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning , 2018, J. Comput. Civ. Eng..

[169]  Wei Wang,et al.  Computer vision-based concrete crack detection using U-net fully convolutional networks , 2019, Automation in Construction.

[170]  Eduardo Zalama Casanova,et al.  Road Crack Detection Using Visual Features Extracted by Gabor Filters , 2014, Comput. Aided Civ. Infrastructure Eng..

[171]  Huy Pham,et al.  Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection , 2018, J. Field Robotics.

[172]  윤태영,et al.  Transportation Research Board of the National Academies , 2015 .

[173]  Nenad Gucunski,et al.  Delamination and concrete quality assessment of concrete bridge decks using a fully autonomous RABIT platform , 2015 .

[174]  António E. Ruano,et al.  GPR target detection using a neural network classifier designed by a multi-objective genetic algorithm , 2019, Appl. Soft Comput..

[175]  Hung Manh La,et al.  Rebar detection and localization for bridge deck inspection and evaluation using deep residual networks , 2020 .

[176]  Shima Taheri,et al.  A review on five key sensors for monitoring of concrete structures , 2019, Construction and Building Materials.

[177]  George Nikolakopoulos,et al.  On Model-based Adhesion Control of a Vortex Climbing Robot , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[178]  Hyun Myung,et al.  Development of a Wall-Climbing Drone Capable of Vertical Soft Landing Using a Tilt-Rotor Mechanism , 2019, IEEE Access.

[179]  Takahide Sakagami,et al.  Nondestructive Evaluation of Fatigue Cracks in Steel Bridges Based on Thermoelastic Stress Measurement , 2016 .

[180]  Glenn Washer,et al.  Field Testing of Hand-Held Infrared Thermography, Phase II , 2015 .

[181]  Takahiro Yamaguchi,et al.  Sensitive Damage Detection of Reinforced Concrete Bridge Slab by “Time-Variant Deconvolution” of SHF-Band Radar Signal , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[182]  Lorenzo Capineri,et al.  Advanced image-processing technique for real-time interpretation of ground-penetrating radar images , 1998, Int. J. Imaging Syst. Technol..

[183]  Roberto Cipolla,et al.  MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving , 2016, 2018 IEEE Intelligent Vehicles Symposium (IV).

[184]  Raúl Fangueiro,et al.  Advanced Composite Materials for Aerospace Engineering: Processing, Properties and Applications , 2016 .

[185]  Paul Chinowsky,et al.  Climate change risks to US infrastructure: impacts on roads, bridges, coastal development, and urban drainage , 2015, Climatic Change.

[186]  W Spencer Guthrie,et al.  Rapid Multichannel Impact-Echo Scanning of Concrete Bridge Decks from a Continuously Moving Platform , 2017 .

[187]  Martin Fritzsche Detection of buried landmines using ground penetrating radar , 2017 .

[188]  Joseph Moysan,et al.  Improvement of the non-destructive evaluation of plasma facing components by data combination of infrared thermal images , 2007 .

[189]  Sven Birkenfeld Automatic detection of reflexion hyperbolas in gpr data with neural networks , 2010, 2010 World Automation Congress.

[190]  Ying Zhang,et al.  Ensemble empirical mode decomposition of impact-echo data for testing concrete structures , 2012 .

[191]  Nhan H. Pham,et al.  Design and implementation of an autonomous robot for steel bridge inspection , 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[192]  Shuai Guo,et al.  Sewer damage detection from imbalanced CCTV inspection data using deep convolutional neural networks with hierarchical classification , 2019, Automation in Construction.

[193]  Bachir Boudraa,et al.  Automatic Crack Detection and Characterization During Ultrasonic Inspection , 2010 .

[194]  Robert S. Kirk,et al.  Highway Bridge Conditions: Issues for Congress , 2013 .

[195]  Jingang Yi,et al.  Autonomous robotic system for high-efficiency non-destructive bridge deck inspection and evaluation , 2013, 2013 IEEE International Conference on Automation Science and Engineering (CASE).

[196]  Zheng Liu,et al.  Survey: State of the Art in NDE Data Fusion Techniques , 2007, IEEE Transactions on Instrumentation and Measurement.

[197]  Sarah Bergbreiter,et al.  Bridge risk investigation diagnostic grouped exploratory (BRIDGE) bot , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[198]  Kelvin C. P. Wang,et al.  Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V , 2020, IEEE Transactions on Intelligent Transportation Systems.

[199]  Huanhuan Chen,et al.  Probabilistic robust hyperbola mixture model for interpreting ground penetrating radar data , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[200]  Brent M. Phares,et al.  Evaluation of Air-Coupled Impact-Echo Test Method , 2015 .

[201]  Wenfu Xu,et al.  A small climbing robot for the intelligent inspection of nuclear power plants , 2014, 2014 4th IEEE International Conference on Information Science and Technology.

[202]  Ning Ding,et al.  A Biologically Inspired Cable Climbing Robot: CCRobot - Design and Implementation , 2018, 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[203]  Mu-Chun Su,et al.  A self organizing map optimization based image recognition and processing model for bridge crack inspection , 2017 .

[204]  Jean-Louis Briaud,et al.  Probability of scour depth exceedance owing to hydrologic uncertainty , 2007 .

[205]  Harry M. Jol,et al.  Texture-based classification of ground-penetrating radar images , 2006 .

[206]  Moncef L. Nehdi,et al.  Application of Passive Infrared Thermography for the Detection of Defects in Concrete Bridge Elements , 2016 .

[207]  Inbok Lee,et al.  The effective near-surface defect identification by dynamic behavior associated with both impact-echo and flexural modes for concrete structures , 2018 .

[208]  William M Moore,et al.  AN INSTRUMENT FOR DETECTING DELAMINATION IN CONCRETE BRIDGE DECKS , 1970 .

[209]  Devin K. Harris,et al.  Combined Imaging Technologies for Concrete Bridge Deck Condition Assessment , 2015 .

[210]  J. Ashlock,et al.  Comparison of MASW and MSOR for Surface Wave Testing of Pavements , 2015 .

[211]  Yang Liu,et al.  Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network , 2017, Comput. Aided Civ. Infrastructure Eng..

[212]  Hong Zhang,et al.  A Modular Biped Wall-Climbing Robot With High Mobility and Manipulating Function , 2013, IEEE/ASME Transactions on Mechatronics.

[213]  Joseph N. Wilson,et al.  A Large-Scale Systematic Evaluation of Algorithms Using Ground-Penetrating Radar for Landmine Detection and Discrimination , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[214]  Hung Manh La,et al.  Concrete Crack Pixel Classification Using an Encoder Decoder Based Deep Learning Architecture , 2019, ISVC.

[215]  J. Ioannidis,et al.  The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. , 2009, Journal of clinical epidemiology.

[216]  Weihua Sheng,et al.  A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance , 2014, IEEE Transactions on Automation Science and Engineering.

[217]  Hung Manh La,et al.  Rebar Detection and Localization for Non-destructive Infrastructure Evaluation of Bridges Using Deep Residual Networks , 2019, ISVC.

[218]  Marvin W. Halling,et al.  Bridge Failure Rate , 2015 .

[219]  Yoshihide Sekimoto,et al.  Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[220]  Shin'ichi Yuta,et al.  Semi-autonomous Collision Avoidance Flight Using Two Dimensional Laser Range Finder with Mirrors for Bridge Inspection , 2018, 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[221]  MengChu Zhou,et al.  Automatic Detection of Bridge Deck Condition From Ground Penetrating Radar Images , 2011, IEEE Transactions on Automation Science and Engineering.

[222]  Jiang Honghai,et al.  Detection of surface crack defects on ferrite magnetic tile , 2014 .

[223]  Lars Schmidt-Thieme,et al.  Pipe localization by apex detection , 2012 .

[224]  G. Antes,et al.  Five Steps to Conducting a Systematic Review , 2003, Journal of the Royal Society of Medicine.

[225]  Yoshihide Sekimoto,et al.  Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[226]  G. Caprari,et al.  Climbing robot for corrosion monitoring of reinforced concrete structures , 2012, 2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI).

[227]  Hung Manh La,et al.  A Mobile Robot for Automated Civil Infrastructure Inspection and Evaluation , 2018, 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).