15. References

15. References

[1] "ASCE'S 2017 infrastructure report card: bridges," 2017. [Online]. Available: https://www.infrasturecturereportcard.org/cat-item/bridges/ .

[2] J. Fleming, "Bridge Management System (BMS2) Coding Manual (Publication 100A)," 2019.

[3] A. Ellenberg, A. Kontsos, I. Bartoli and A. Pradhan, "Masonry Crack Detection Application of an Unmanned Aerial Vehicle," in International Conference on Computing in Civil and Building Engineering, 2014.

[4] H. Kim, S. Sim and S. Cho, "Unmanned aerial vehicle (UAV)powered concrete crack detection based on digital image processing," in 6th International Conference on Advances in Experimental Structural Engineering, Chicago, 2015.

[5] S. Iyer and S. K. Sinha, "A robust approach for automatic detection and segmentation of cracks in underground pipeline images," Image Vis. Comput., vol. 23, no. 10, p. 921–933, 2005.

[6] M. Salman, S. Mathavan, K. Kamal and M. Rahman, "Pavement crack detection using the Gabor filter," in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 2013.

[7] A. M. Talab, Z. Huang, F. Xi and L. Ming, "Detection crack in image using Otsu method and multiple filtering in image processing techniques," Optik, vol. 127, no. 3, p. 1030–1033, 2016.

[8] B. Shan, S. Zheng and J. Ou, "A stereovision-based crack width detection approach for concrete surface assessment," KSCE J. Civ. Eng., vol. 20, no. 2, p. 803–812, 2016.

[9] S. K. Sinha and P. W. Fieguth, "Automated detection of cracks in buried concrete pipe images," Autom. Constr., vol. 15, no. 1, p. 58–72, 2006. [10] Q. Zou, Y. Cao, Q. Li, Q. Mao and S. Wang, "CrackTree: Automatic crack detection from pavement images," Pattern Recognit. Lett., vol. 33, no. 3, p. 227–238, 2012. [11] Y. Fujita and Y. Hamamoto, "A robust automatic crack detection method from noisy concrete surfaces," Mach. Vis. Appl., vol. 22, no. 2, p. 245–254, 2011.

[12] J. B. Butcher, C. R. Day, J. C. Austin, P. W. Haycock, D. Verstraeten and B. Schrauwen, "Defect detection in reinforced concrete using random neural architectures," Comput. Aided Civ. Infrastruct. Eng., vol. 29, no. 3, p. 191–207, 2014.

[13] X. Jiang and H. Adeli, "Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings," Int. J. Numer. Methods Eng., vol. 71, no. 5, p. 606–629, 2007.

[14] S. W. Liu, J. H. Huang, J. C. Sung and C. C. Lee, "Detection of cracks using neural networks and computational mechanics," Comput. Methods Appl. Mech. Eng., vol. 191, no. 2526, p. 2831–2845, 2002.

[15] H. Moon and J. Kim, "Intelligent crack detecting algorithm on the concrete crack image using neural network," in 28th ISARC, 2011. [16] M. O’Byrne, B. Ghosh, F. Schoefs and V. Pakrashi, "Regionally enhanced multiphase segmentation technique for damaged surfaces," Comput.-Aided Civ. Infrastruct. Eng., vol. 29, no. 9, p. 644–658, 2014.

[17] M. Maguire, S. Dorafshan and S. Thomas, "SDNET2018: a concrete crack image dataset for machine learning applications," Logan: Utah State, 2018.

[18] A. Das, "Interpretation and processing of image in frequency domain. In: Guide to Signals and Patterns in Image Processing," Berlin, Springer, 2015, p. 93–147. [19] M. Abadi , A. Agarwal, P. Barham, E. Brevdo, Z. Chen and C. Citro, "TensorFlow: large-scale machine learning on heterogeneous distributed systems," arXiv, p. 1603.04467, 2016. [20] Q. Zhang, K. Barri, S. K. Babanajad and A. H. Alavi, "Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain," Engineering, p. DOI: https://doi.org/10.1016/j.eng.2020.07.026, 2020. [21] A. Buades, B. Coll and J. M. Morel, "Non-Local Means Denoising," Image Processing On Line, vol. https://doi.org/10.5201/ipol.2011.bcm_nlm, p. 208–212, 2011.

[22] T. R. Singh, S. Roy, O. I. Singh, T. Sinam and K. M. Singh, "A New Local Adaptive Thresholding Technique in Binarization," International Journal of Computer Science, vol. 8, no. 6, pp. 1694-0814, 2011.

[23] P. J. Clark and F. C. Evans, "Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations," Ecology, vol. 35, no. 4, pp. 445-453, 1954.

[24] Q. Zhang and A. Alavi, "Automated two-stage approach for detection and quantification of surface defects in concrete bridge decks," in Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV, Long Beach, CA, USA, 2021.

[25] N. Gucunski and N. R. Council, "Nondestructive testing to identify concrete bridge deck deterioration," Transportation Research Board, 2013.

[26] S. A. Dabous, S. Yaghi, S. Alkass and O. Moselhi, "Concrete bridge deck condition assessment using IR Thermography and Ground Penetrating Radar technologies," Automation in Construction, vol. 81, pp. 340-354, 2017.

[27] T. Omar and M. L. Nehdi, "Clustering-Based Threshold Model for Condition Assessment of Concrete Bridge Decks Using Infrared Thermography," in Proc., International Congress and

Exhibition" Sustainable Civil Infrastructures: Innovative Infrastructure Geotechnology", Springer, 2017.

[28] G. Washer, R. Fenwick, S. Nelson and R. Rumbayan, "Guidelines for thermographic inspection of concrete bridge components in shaded conditions," Transportation Research Record: Journal of the Transportation Research Board, pp. 13-20, 2013.

[29] G. G. Clemena and W. T. McKeel, "Detection of Delamination in Bridge Decks with Infrared," Transportation Research Record, vol. 1, pp. 180-182, 1978.

[30] M. Everingham, L. VanGool, C. K. Williams, J. Winn and A. Zisserman , "The PASCAL Visual Object Classes (VOC) Challenge 2012," International Journal of Computer Vision, vol. 2, no. 88, pp. 303-338, 2010.

[31] M. Cordts, M. Omran, S. Ramos, T. Rehfeld , M. Enzweiler, R. Benenson, U. Franke, S. Roth and B. Schiele, "The cityscapes dataset for semantic urban scene understanding," in IEEE Conference on Computer Vision and Patter Recognition (CVPR), Las Vegas, USA, 2016.

[32] E. McLaughlin, N. Charron and S. Narasimhan, "Combining Deep Learning and Robotics for Automated Concrete Delamination Assessment," ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, vol. 36, pp. 485-492, 2019.

[33] MATLAB, "version 7.10.0 (2019a)," in The MathWorks Inc, 2019. [34] M. Abadi, A. Agarwal, P. Barham, E. Brevdo , Z. Chen, C. Citro and et al, "TensorFlow: large-scale machine learning on heterogeneous distributed systems," arXiv:1603.04467, 2016.

[35] Q. Zhang, K. Barri and Z. Wan, "A Deep Learning-based Autonomous System for Detection and Quantification of Delamination on Concrete Bridge Decks," in International Bridge Conference, Pittsburgh, PA, USA, 2021.

[36] F. Lundh, "An introduction to tkinter," URL: www. pythonware. com/library/tkinter/introduction/index. htm, 1999.

[37] M. J. Sansalone and W. B. Streett, "Impact-echo," Nondestructive Evaluation of Concrete and Masonry , 1997.

[38] J. J. Daniels, "Ground Penetrating Radar Fundamentals," Prepared as an appendix to a report to the U.S.EPA, Region V, 2000.

[39] B. Elsener, C. Andrade , J. Gulikers, R. Polder and M. Raupach, "Half-cell potential measurements- Potential mapping on reinforced concrete structures," Material and Structures, pp. 461-471, 2003.

[40] S. Lee, N. Kalos and D. H. Shin, "Non-Destructive Testing Methods in the U.S. for Bridge Inspection and Maintenance," KSCE Journal of Civil Engineering , vol. 18, no. 5, pp. 1322-1331, 2014.

[41] M. R. Clark, D. M. McCann and M. C. Forde, "Application of infrared thermography to the non-destructive testing of concrete and masonry bridges," NDT & E International, vol. 36, no. 4, p. 265–275, 2003.

[42] Q. Zhang and A. Alavi, "Automated two-stage approach for detection and quantification of surface defects in concrete bridge decks," in Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV, Long Beach, 2021.

[43] A. A. Hesse, R. A. Atadero and M. E. Ozbek, "Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV," Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV, vol. 20, no. 11, 2015.

[44] M. Ahmed, O. Moselhi and A. Bhowmick, "Two-tier data fusion method for bridge condition assessment," Canadian Journal of Civil Engineering, vol. 45, no. 3, 2018.

[45] S. Babanajad and F. Jalinoos, "A Framework for Assessing Corrosion and Damage in Concrete Bridge Decks Using Multi-Sensor NDE Data," In Preparation for peer-review journal submission, 2021.

[46] F. Jalinoos, S. Babanajad and F. Moon, "QC/QA PROCEDURES FOR NDE CONTOUR MAPS COLLECTED THROUGH THE LTBP PROGRAM," FHWA Internal Technical Report , Office of Infrastructure Research and Development, FHWA, Washington DC, 2020.

[47] S. Yaghi, "Integrated remote sensing technologies for condition assessment of concrete bridges," M.Sc. dissertation, Department of Building, Civil and Environmental Engineering, Concordia University., 2014.

[48] Y. Deng, "A threat assessment model under uncertain environment," Math. Probl. Eng, vol. 201, pp. 1-12, 2015.

[49] C. R. Parikh, M. J. Pont and N. B. Jones, "Application of Dempster–Shafer theory in condition monitoring applications: A case study," Pattern Recognit. Lett, vol. 22, pp. 777-785, 2001.

[50] X. F. Fan and M. J. Zuo, "Fault diagnosis of machines based on D-S evidence theory. Part 2: Application of the improved D-S evidence theory in gearbox fault diagnosis," Pattern Recognit. Lett, vol. 27, pp. 377-385, 2006.

[51] G. Dong and G. Kuang, "Target Recognition via Information Aggregation Through Dempster–Shafer’s Evidence Theory," IEEE Geosci. Remote Sens. Lett, vol. 12, pp. 1247-1251, 2015.

[52] J. Hu, Z. Yu, X. Zhai and J. Peng, "Research of decision fusion diagnosis of aero-engine rotor fault based on improved D-S theory," Acta Aeronautica et Astronautica Sinica, vol. 35, pp. 436-443, 2014.

[53] B. Zhang, "Study on image fusion based on different fusion rules of wavelet transform," 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), pp. V3-649-V3-653, 2010.

[54] Q. Zhang and A. Alavi, "Improving Bridge Assessment via Fusion of Multi-resource Nondestructive Evaluation," in Sreuctures Congress, Atlanta, GA,USA, 2022.

[55] H. Shakhatreh, A. H. Sawalmeh, A. AI-Fuqaha, Z. Dou, E. Almaita, I. Khalil, N. S. Othman , A. Khreishah and M. Guizani , "Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges," IEEE Access , vol. 7, pp. 48572 - 48634, 2019.

[56] P. S. Ramesh and J. V. Muruga Lal Jeyan, "Comparative analysis of the impact of operating parameters on military and civil applications of mini unmanned aerial vehicle (UAV)," in AIP Conference Proceedings, 2020.

[57] E. Jeong, J. Seo and J. Wacker, "Literature Review and Technical Survey on Bridge Inspection Using Unmanned Aerial Vehicles," Journal of Performance of Constructed Facilities, vol. 34, no. 6, 2020.

[58] S. Hiasa, R. Birgul and F. N. Catbas, "Infrared thermography for civil structural assessment: demonstrations with laboratory and field studies," J. Civ. Struct. Health Monit., vol. 6, no. 3, pp. 619-636, 2016.

[59] G. Washer, R. Fenwick, S. Nelson and R. Rumbayan, "Guidelines for the thermographic inspection of concrete bridge components in shaded conditions," Transportation Research Record , pp. 12-20, 2013.

[60] Q. Zhang, S. H. Ro, J. Gong, F. Moon and A. Alavi, "Recent Advances in Bridge Condition Assessment Using Unmanned Aerial Vehicles," in International Workshop of Structural Health Monitoring, Stanford, CA, USA, 2022.

[61] LTBP InfoBridge, "Long-term Bridge Performance Program (LTBP) online webpage," Federal Highway Administration, [Online]. Available: https://infobridge.fhwa.dot.gov/Home. [62] N. Gucunski, G. R. Consolazio and A. Maher, "Concrete Bridge Deck Delamination Detection by Integrated Ultrasonic Methods," International Journal of Materials and Product Technology, vol. 26, no. 1-2, pp. 19-34, 2006. [63] K. R. Maser and A. Rawson, "Network Bridge Deck Surveys Using High Speed Radar: Case Studies of 44 Decks (Abridgement)," Transportation Research Record, vol. 1347, pp. 25-28, 1992.

[64] N. Gucunski, B. Pailes, J. Kim, H. Azari and K. Dinh, "Capture and Quantification of Deterioration Progression in Concrete Bridge Decks through Periodical NDE Surveys," Journal of Infrastructure Systems, vol. 23, no. 1, pp. 1-11, 2016. [65] G. Stott, "Structural Inspections for High Mast Lighting," Advanced Infrastructure Design, Inc., 2021.

[66] B. J. Perry, Y. Guo, R. Atadero and J. W. van de Lindt, "Streamlined bridge inspection system utilizing unmanned aerial vehicles (UAVs) and machine learning," Measurement, vol. 164, p. 108048, 2020.

[67] Y. LeCun, Y. Bengio and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, p. 436–444, 2015.

[68] D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella and J. Schmidhuber, "Flexible, high performance convolutional neural networks for image classification," in Twenty-Second International Joint Conference on Artificial Intelligence, 2011.

[69] D. Scherer, A. Muller and S. Behnke, "Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition," in 20th International Conference on Artificial Neural Networks (ICANN), 2010.

[70] J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Netw, vol. 61, p. 85–117, 2015.

[71] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus and Y. LeCun, "Overfeat: Integrated recognition, localization and detection using convolutional networks," ArXiv, p. Prepr. ArXiv13126229, 2013.

[72] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput, vol. 9, no. 8, p. 1735–80, 1997. [73] F. A. Gers and E. Schmidhuber, "LSTM recurrent networks learn simple context-free and context-sensitive languages," IEEE Trans Neural Netw, vol. 12, no. 6, p. 1333–40, 2001.

[74] G. Swapna, K. Soman and R. Vinayakumar, "Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals," Procedia Comput Sci, vol. 132, p. 1253–62, 2018.

[75] J. Zhao, X. Mao and L. Chen, "Speech emotion recognition using deep 1D & 2D CNN LSTM networks," Biomed Signal Process Control, vol. 47, p. 312–23, 2019.

[76] S. Chang, B. Yu and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Trans Image Process, vol. 9, no. 9, p. 1532–46, 2000.

[77] M. Zhang and B. K. Gunturk, "Multiresolution bilateral filtering for image denoising," IEEE Trans Image Process, vol. 17, no. 12, p. 2324–33, 2008.

[78] J. Portilla, V. Strela, M. J. Wainwright and E. P. Simoncelli , "Image denoising using scale mixtures of Gaussians in the wavelet domain," IEEE Trans Image Process, vol. 12, no. 11, p. 1338–51, 2013.

[79] R. C. Gonzalez, R. E. Woods and B. Masters, Digital image processing third edition, Upper Saddle River: Prentice Hal, 2008.

[80] A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Advances in Neural Information Processing Systems 25 (NIPS 2012), 2012.

[81] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv:1409.1556, 2015.

[82] F. Chollet, "Xception: Deep Learning With Depthwise Separable Convolutions," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[83] L. Chen, Y. Zhu, G. Papandreou, F. Schroff and H. Adam, "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation," in European Conference on Computer Vision (ECCV), 2018.

[84] T. Corpetti and O. Planchon , "Front detection on satellite images based on wavelet and evidence theory: Application to the sea breeze fronts," Remote Sensing of Environment, vol. 115, p. 306–324, 2011.

[85] Q. Jiang, X. Jin, S. J. Lee and S. Yao, "A Novel Multi-Focus Image Fusion Method Based on Stationary Wavelet Transform and Local Features of Fuzzy Sets," IEEE Access, vol. 5, pp. 20286-20302, 2017.

[86] L. Guo, X. Cao and L. Liu, "Dual-tree biquaternion wavelet transform and its application to color image fusion," Signal Processing , vol. 171, p. 107513, 2020. [87] E. J. Stollnitz, T. D. DeRose and D. H. Salesin, "Wavelets for computer graphics: a primer, part 1," IEEE Comput. Graphics. Appl, vol. 15, no. 3, p. 76–84, 1995.

[88] G. Pajares and J. M. Cruz, "A wavelet-based image fusion tutorial," Pattern Recognition, vol. 37, no. 9, pp. 1855-1872, 2004.

[89] Y. Deng, "Deng entropy," Chaos Solitons Fractals, vol. 91, pp. 549-553, 2016.

[90] A. P. Dempster, "Upper and Lower Probabilities Induced by a Multivalued Mapping," The Annals of Mathematical Statistics, vol. 38, no. 2, pp. 325-339, 1967.

[91] G. Shafer, "A Mathematical Theory of Evidence," Princeton University Press, 1976. [92] T. L. Fine, "Review: Glenn Shafer, A mathematical theory of evidence," The Bulletin of the American Mathematical Society, vol. 83, no. 4, pp. 667-672, 1977. [93] F. Ye, J. Chen and Y. Li, "Improvement of DS Evidence Theory for Multi-Sensor Conflicting Information," Symmetry, vol. 9, no. 5, p. 69, 2017.

[94] S. Zhang, Q. Pan and H. Zhang, " A New Kind of Combination Rule of Evidence Theory," Control Decis, vol. 15, pp. 540-544, 2000.

[95] C. H. Hugenholtz, K. Whitehead, O. W. Brown, T. E. Barchyn, B. J. Moorman, A. LeClair, K. Riddell and T. Hamilton, "Geomorphological mapping with a small unmanned

aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetricallyderived digital terrain model," Geomorphology, vol. 194, pp. 16-24, 2013.

[96] F. Nex, "UAV photogrammetry for mapping and 3d modeling–current status and future perspectives," in International archives of the photogrammetry, remote sensing and spatial information sciences, 2010.

[97] K. Whitehead, B. J. Moorman and C. H. Hugenholtz, "Low-cost, on-demand aerial photogrammetry for glaciological measurement," in Cryosphere Discussions , 7, 2013.