A railway tunnel structural monitoring methodology proposal for predictive maintenance
暂无分享,去创建一个
Paulo J. Tavares | Pedro M.G.P. Moreira | Behzad V. Farahani | Francisco Barros | Pedro J. Sousa | Paulo J. Tavares | Francisco Barros | Francisco Barros | P. Moreira | Pedro M.G.P. Moreira
[1] Paulo J. Tavares,et al. A coupled 3D laser scanning and digital image correlation system for geometry acquisition and deformation monitoring of a railway tunnel , 2019, Tunnelling and Underground Space Technology.
[2] Tadeusz Uhl,et al. Vision‐based algorithms for damage detection and localization in structural health monitoring , 2016 .
[3] Carlos Balaguer,et al. Tunnel structural inspection and assessment using an autonomous robotic system , 2018 .
[4] Ingo Neumann,et al. An automatic and intelligent optimal surface modeling method for composite tunnel structures , 2019, Composite Structures.
[5] Hui Li,et al. Automatic seismic damage identification of reinforced concrete columns from images by a region‐based deep convolutional neural network , 2019, Structural Control and Health Monitoring.
[6] Paulo J. Tavares,et al. Compact tension fracture specimen: Experimental and computational implementations on stress intensity factor , 2018 .
[7] Paulo J. Tavares,et al. A nonlinear simulation of a bi-failure specimen through improved discretisation methods: A validation study , 2018, The Journal of Strain Analysis for Engineering Design.
[8] Branko Glisic,et al. Crack detection and characterization techniques—An overview , 2014 .
[9] Ivan Bartoli,et al. Full‐field deformation measurements during seismic loading of masonry buildings , 2017 .
[10] Li Zhang,et al. The lining quality detection and grouting effect evaluation of the Kunlun Mountain railway tunnel using ground-penetrating radar , 2008 .
[11] S. Sumitro,et al. Sustainable structural health monitoring system , 2005 .
[12] Paulo J. Tavares,et al. A digital image correlation analysis on a sheet AA6061-T6 bi-failure specimen to predict static failure , 2018, Engineering Failure Analysis.
[13] Shirley J. Dyke,et al. Experimental validation of structural health monitoring for flexible bridge structures , 2005 .
[15] Paulo J. Tavares,et al. Material characterization and damage assessment of an AA5352 aluminium alloy using digital image correlation , 2020 .
[16] Shaohua Wang,et al. Railway Tunnel Clearance Inspection Method Based on 3D Point Cloud from Mobile Laser Scanning , 2017, Sensors.
[17] Paulo J. Tavares,et al. Concept of stress dead zone in cracked plates: Theoretical, experimental, and computational studies , 2019, Fatigue & Fracture of Engineering Materials & Structures.
[18] Zhengyou Zhang,et al. A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[19] Suzanne Lacasse,et al. Multi-sensor data fusion based assessment on shield tunnel safety , 2019 .
[20] David Mas,et al. A method to measure small local strains in concrete surfaces using its natural texture and image cross‐correlation , 2019, Structural Control and Health Monitoring.
[21] Ivan Bartoli,et al. Multiscale deformation measurements using multispectral optical metrology , 2018 .
[22] Sung Ho Hwang,et al. Estimation of crack width based on shape‐sensitive kernels and semantic segmentation , 2019, Structural Control and Health Monitoring.
[23] Luis Saucedo-Mora,et al. Contactless safety evaluation of damaged structures through energetic criteria , 2018 .
[24] Ayan Sadhu,et al. A literature review of next‐generation smart sensing technology in structural health monitoring , 2019, Structural Control and Health Monitoring.
[25] Zilong Zou,et al. Utilization of structural health monitoring in long‐span bridges: Case studies , 2017 .
[26] Qingchuan Zhang,et al. Creep of stainless steel under heat flux cyclic loading ( 500-1000 ° C ) with different mechanical preloads in a vacuum environment using 3 D-DIC , 2019 .
[27] Francesco Cadini,et al. Particle filtering‐based adaptive training of neural networks for real‐time structural damage diagnosis and prognosis , 2019, Structural Control and Health Monitoring.
[28] Jian Hong Wang. Lifecycle cost and performance analysis for repair of concrete tunnels , 2018 .
[29] Chang-Soo Han,et al. Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel , 2007 .
[30] Xin Huang,et al. Capturing the cracking characteristics of concrete lining during prototype tests of a special-shaped tunnel using 3D DIC photogrammetry , 2018 .
[31] Yun Liu,et al. Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring , 2014, Sensors.
[32] Pedro Pazzoto Cacciari,et al. Modeling a Shallow Rock Tunnel Using Terrestrial Laser Scanning and Discrete Fracture Networks , 2017, Rock Mechanics and Rock Engineering.
[33] Paulo J. Tavares,et al. Elastoplastic response and failure assessment of steel alloys: Empirical and computational analyses , 2018, Fatigue & Fracture of Engineering Materials & Structures.
[34] Daniele Perissin,et al. Evaluation of InSAR monitoring data for post‐tunnelling settlement damage assessment , 2018, Structural Control and Health Monitoring.
[35] Evelyne Toussaint,et al. Analysis of Cracks and Deformations in a Full Scale Reinforced Concrete Beam Using a Digital Image Correlation Technique , 2011 .
[36] Pedro Pazzoto Cacciari,et al. Mapping and characterization of rock discontinuities in a tunnel using 3D terrestrial laser scanning , 2015, Bulletin of Engineering Geology and the Environment.
[37] Jun Teng,et al. Optimal sensor placement for bridge damage detection using deflection influence line , 2020 .
[38] Sinan Acikgoz,et al. Sensing dynamic displacements in masonry rail bridges using 2D digital image correlation , 2018 .
[39] Marko Pejić,et al. Design and optimisation of laser scanning for tunnels geometry inspection , 2013 .