A novel intelligent inspection robot with deep stereo vision for three-dimensional concrete damage detection and quantification

Crack assessment of reinforced concrete structures using stereo cameras is a potential way for increasing the efficiency and safety of infrastructure maintenance routines. However, existing damage ...

[1]  Dimos Polyzois,et al.  Deep learning-based automatic volumetric damage quantification using depth camera , 2019, Automation in Construction.

[2]  J. Zhang,et al.  Mobile Impact Testing for Structural Flexibility Identification with only a Single Reference , 2015, Comput. Aided Civ. Infrastructure Eng..

[3]  Dongho Kang,et al.  Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging , 2018, Comput. Aided Civ. Infrastructure Eng..

[4]  Pedro F. Silva,et al.  Cyclic Crack Monitoring of a Reinforced Concrete Column under Simulated Pseudo-Dynamic Loading Using Piezoceramic-Based Smart Aggregates , 2016 .

[5]  Hong Hao,et al.  High‐resolution time‐frequency representation for instantaneous frequency identification by adaptive Duffing oscillator , 2020, Structural Control and Health Monitoring.

[6]  Luh-Maan Chang,et al.  Support-vector-machine-based method for automated steel bridge rust assessment , 2012 .

[7]  Namgyu Kim,et al.  Deep learning–based autonomous concrete crack evaluation through hybrid image scanning , 2019, Structural Health Monitoring.

[8]  Gao Fan,et al.  Vibration signal denoising for structural health monitoring by residual convolutional neural networks , 2020 .

[9]  Yang Yu,et al.  Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN , 2019, Comput. Electron. Agric..

[10]  Billie F. Spencer,et al.  Vision‐based automated bridge component recognition with high‐level scene consistency , 2019, Comput. Aided Civ. Infrastructure Eng..

[11]  Alessandra Mileo,et al.  Real-time data analytics and event detection for IoT-enabled communication systems , 2017, J. Web Semant..

[12]  Günter Last,et al.  Inclusion–Exclusion Principles for Convex Hulls and the Euler Relation , 2016, Discret. Comput. Geom..

[13]  Young-Jin Cha,et al.  Vision-based detection of loosened bolts using the Hough transform and support vector machines , 2016 .

[14]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[15]  Ayan Sadhu,et al.  A literature review of next‐generation smart sensing technology in structural health monitoring , 2019, Structural Control and Health Monitoring.

[16]  Yuequan Bao,et al.  Video‐based multiscale identification approach for tower vibration of a cable‐stayed bridge model under earthquake ground motions , 2019, Structural Control and Health Monitoring.

[17]  Jin-Hwan Lee,et al.  Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network , 2020 .

[18]  Zhangting Wu,et al.  Structural identification and damage detection through long-gauge strain measurements , 2015 .

[19]  M. N. Noorsuhada.  An overview on fatigue damage assessment of reinforced concrete structures with the aid of acoustic emission technique , 2016 .

[20]  Hui Li,et al.  Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images , 2019 .

[21]  B. F. Spencer,et al.  Structural Displacement Measurement Using an Unmanned Aerial System , 2018, Comput. Aided Civ. Infrastructure Eng..

[22]  Gangbing Song,et al.  Very early age concrete hydration characterization monitoring using piezoceramic based smart aggregates , 2013 .

[23]  Yali Ren,et al.  Design of a new low-cost unmanned aerial vehicle and vision-based concrete crack inspection method , 2020 .

[24]  Alison B. Flatau,et al.  Review Paper: Health Monitoring of Civil Infrastructure , 2003 .

[25]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[26]  Miriam A. M. Capretz,et al.  A systematic review of convolutional neural network-based structural condition assessment techniques , 2021 .

[27]  Yicheng Li,et al.  A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects , 2018, Comput. Aided Civ. Infrastructure Eng..

[28]  Wensu Chen,et al.  Finite element modelling of dynamic bonding behaviours between fibre reinforced polymer sheet and concrete , 2020 .

[29]  Hui Li,et al.  The State of the Art of Data Science and Engineering in Structural Health Monitoring , 2019, Engineering.

[30]  John E. T. Penny,et al.  Crack Modeling for Structural Health Monitoring , 2002 .

[31]  Hui Li,et al.  SMC structural health monitoring benchmark problem using monitored data from an actual cable‐stayed bridge , 2014 .

[32]  Moncef L. Nehdi,et al.  Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography , 2017 .

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

[34]  Hui Li,et al.  Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio , 2018 .

[35]  Billie F. Spencer,et al.  Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring , 2019, Engineering.

[36]  Jian Zhang,et al.  Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system , 2019, Comput. Aided Civ. Infrastructure Eng..

[37]  Pradeep Kumar,et al.  Flaw detection in radiographic weldment images using morphological watershed segmentation technique , 2009 .

[38]  Jerome P. Lynch,et al.  A summary review of wireless sensors and sensor networks for structural health monitoring , 2006 .

[39]  Jian Zhang,et al.  Pixel‐level crack delineation in images with convolutional feature fusion , 2018, Structural Control and Health Monitoring.