On the crack characterization of reinforced concrete structures: Experimental and data-driven numerical study
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Dibyendu Adak | Shubhankar Majumdar | Subhrajit Dutta | Sandeep Das | S. Majumdar | Sandeep Das | D. Adak | Subhrajit Dutta
[1] Abílio M. P. De Jesus,et al. Strain-life and crack propagation fatigue data from several Portuguese old metallic riveted bridges , 2011 .
[2] Weiwen Peng,et al. Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty , 2016, Reliab. Eng. Syst. Saf..
[3] R. Ian Gilbert. Control of Flexural Cracking in Reinforced Concrete , 2008 .
[4] N. Jovicic,et al. Numerical Modeling of Crack Growth Using the Level Set Fast Marching Method , 2005 .
[5] Byung Hwan Oh,et al. Advanced Crack Width Analysis of Reinforced Concrete Beams under Repeated Loads , 2007 .
[6] Fei Kang,et al. Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation , 2019, Engineering Structures.
[7] James A. Sethian,et al. Level Set Methods and Fast Marching Methods , 1999 .
[8] W. Qu,et al. Experimental Study of Fatigue Flexural Performance of Concrete Beams Reinforced with Hybrid GFRP and Steel Bars , 2017 .
[9] E. Kreyszig,et al. Advanced Engineering Mathematics. , 1974 .
[10] Donato Sabia,et al. A machine learning approach for the automatic long-term structural health monitoring , 2018, Structural Health Monitoring.
[11] Ronald Fedkiw,et al. Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.
[12] Hanna M. Makhlouf,et al. The Effect of Thick Concrete Cover on the MaximumFlexural Crack Width under Service Load , 1996 .
[13] Dongming Zhang,et al. Deep learning based image recognition for crack and leakage defects of metro shield tunnel , 2018, Tunnelling and Underground Space Technology.
[14] Phil Lewis,et al. Characterization of Steel Bridge Superstructure Deterioration through Data Mining Techniques , 2017 .
[15] Changbin Joh,et al. Structural behavior of ultra high performance concrete beams subjected to bending , 2010 .
[16] Claudomiro Sales,et al. Machine learning algorithms for damage detection: Kernel-based approaches , 2016 .
[17] Billie F. Spencer,et al. Concrete Crack Assessment Using Digital Image Processing and 3D Scene Reconstruction , 2016, J. Comput. Civ. Eng..
[18] T. Kanstad,et al. A review of literature and code requirements for the crack width limitations for design of concrete structures in serviceability limit states , 2019, Structural Concrete.
[19] Heng Liu,et al. Image-driven structural steel damage condition assessment method using deep learning algorithm , 2019, Measurement.
[20] Sumathi Poobal,et al. Crack detection using image processing: A critical review and analysis , 2017, Alexandria Engineering Journal.
[21] Hans Gesund,et al. Cracking and Bond Slip in Concrete Beams , 1972 .
[22] Athanasios Chasalevris,et al. Identification of multiple cracks in beams under bending , 2006 .
[23] Qiang Zhou,et al. The Algorithm of Concrete Surface Crack Detection Based on the Genetic Programming and Percolation Model , 2019, IEEE Access.
[24] Paolo Spinelli,et al. A review of literature and code formulations for cracking in R/C members , 2018 .
[25] Ning Wang,et al. New Crack Detection Method for Bridge Inspection Using UAV Incorporating Image Processing , 2018, Journal of Aerospace Engineering.
[26] Paul A. Wawrzynek,et al. Probabilistic fatigue damage prognosis using surrogate models trained via three-dimensional finite element analysis , 2017 .
[27] Li Feng,et al. Analytical Approach for Detection of Multiple Cracks in a Beam , 2010 .
[28] Ioannis Brilakis,et al. Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation , 2011 .
[29] Ted Belytschko,et al. Modelling crack growth by level sets in the extended finite element method , 2001 .
[30] Yonghan Ahn,et al. Estimating Compressive Strength of Concrete Using Deep Convolutional Neural Networks with Digital Microscope Images , 2019, J. Comput. Civ. Eng..
[31] Walter H. Gerstle,et al. Crack Growth in Flexural Members--A Fracture Mechanics Approach , 1992 .
[32] R. Gilbert,et al. On the Reliability of Serviceability Calculations for Flexural Cracked Reinforced Concrete Beams , 2018 .
[33] R. Nigam,et al. Crack detection in a beam using wavelet transform and photographic measurements , 2020, Structures.
[34] Brahim Benmokrane,et al. Flexural Behavior and Serviceability of Normal- and High-Strength Concrete Beams Reinforced with Glass Fiber-Reinforced Polymer Bars , 2013 .
[35] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[36] M. Słowik. The analysis of failure in concrete and reinforced concrete beams with different reinforcement ratio , 2018, Archive of Applied Mechanics.
[37] Byung Hwan Oh,et al. New Formulas for Maximum Crack Width and Crack Spacing in Reinforced Concrete Flexural Members , 1987 .
[38] Pekka J. Toivanen. New geodosic distance transforms for gray-scale images , 1996, Pattern Recognit. Lett..
[39] Abílio M. P. De Jesus,et al. Local unified probabilistic model for fatigue crack initiation and propagation: Application to a notched geometry , 2013 .
[40] Hong-Zhong Huang,et al. A generalized energy-based fatigue–creep damage parameter for life prediction of turbine disk alloys , 2012 .
[41] Dibyendu Adak,et al. A Data-Driven Physics-Informed Method for Prognosis of Infrastructure Systems: Theory and Application to Crack Prediction , 2020 .
[42] Jian-Guo Nie,et al. Bridge crack extraction method based on image-connected domain , 2013 .
[43] Richard N. White,et al. Initiation of Shear Cracking in Reinforced Concrete Beams with No Web Reinforcement , 1991 .
[44] Richard Szeliski,et al. Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.