PSI - Issue 62

Azadeh Yeganehfallah et al. / Procedia Structural Integrity 62 (2024) 201–208 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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Kim, B., & Cho, S. (2018). Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique. In Sensors (Vol. 18, Issue 10, p. 3452). MDPI AG. https://doi.org/10.3390/s18103452 Yang, G., Liu, K., Zhang, J., Zhao, B., Zhao, Z., Chen, X., & Chen, B. M. (2022). Datasets and processing methods for boosting visual inspection of civil infrastructure: A comprehensive review and algorithm comparison for crack classification, segmentation, and detection. In Construction and Building Materials (Vol. 356, p. 129226). Elsevier BV. https://doi.org/10.1016/j.conbuildmat.2022.129226 Eschmann, C., Kuo, C.H., Kuo, C., & Boller, C. (2012). Unmanned Aircraft Systems for Remote Building Inspection and Monitoring. In Proceedings of the 6th European Workshop on Structural Health Monitoring, Dresden, Germany, Volume 2, pp. 1 – 8. Morgenthal, G., & Hallermann, N. (2014). Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures. In Advances in Structural Engineering (Vol. 17, Issue 3, pp. 289 – 302). SAGE Publications. https://doi.org/10.1260/1369-4332.17.3.289 Kim, H., Lee, J., Ahn, E., Cho, S., Shin, M., & Sim, S.-H. (2017). Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing. In Sensors (Vol. 17, Issue 9, p. 2052). MDPI AG. https://doi.org/10.3390/s17092052 Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., & Shoaib, M. A. (2022). Structural crack detection using deep convolutional neural networks. In Automation in Construction (Vol. 133, p. 103989). Elsevier BV. https://doi.org/10.1016/j.autcon.2021.103989 Yao, Y., Tung, S.-T. E., & Glisic, B. (2014). Crack detection and characterization techniques-An overview. In Structural Control and Health Monitoring (Vol. 21, Issue 12, pp. 1387 – 1413). Hindawi Limited. https://doi.org/10.1002/stc.1655 Zhang, L., Shen, J., & Zhu, B. (2020). A research on an improved Unet-based concrete crack detection algorithm. In Structural Health Monitoring (Vol. 20, Issue 4, pp. 1864 – 1879). SAGE Publications. https://doi.org/10.1177/1475921720940068 Kaseko, M. S., and Ritchie, S. G. (1993). A neural network-based methodology for pavement crack detection and classification. Transp. Res. Part C Emerg. Technol. 1, 275 – 291. doi:10.1016/0968-090x(93)90002-w Abdel-Qader I, Abudayyeh O, Kelly ME (2003) Analysis of edge-detection techniques for crack identification in bridges. J Comput Civ Eng 17:255 – 263. https://doi.org/10.1061/(asce)0887-3801(2003)17:4(255) Zhang, L., Yang, F., Daniel Zhang, Y., & Zhu, Y. J. (2016). Road crack detection using deep convolutional neural network. In 2016 IEEE International Conference on Image Processing (ICIP). 2016 IEEE International Conference on Image Processing (ICIP). IEEE. https://doi.org/10.1109/icip.2016.7533052 Cha, Y., Choi, W., & Büyüköztürk, O. (2017). Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks. In Computer-Aided Civil and Infrastructure Engineering (Vol. 32, Issue 5, pp. 361 – 378). Wiley. https://doi.org/10.1111/mice.12263 Gucunski, N., Kee, S.-H., La, H., Basily, B., Maher, A., & Ghasemi, H. (2015). Implementation of a Fully Autonomous Platform for Assessment of Concrete Bridge Decks RABIT. In Structures Congress 2015. Structures Congress 2015. American Society of Civil Engineers. https://doi.org/10.1061/9780784479117.032 Lim, R. S., La, H. M., & Sheng, W. (2014). A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance. In IEEE Transactions on Automation Science and Engineering (Vol. 11, Issue 2, pp. 367 – 378). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tase.2013.2294687 Yokoyama, S., & Matsumoto, T. (2017). Development of an Automatic Detector of Cracks in Concrete Using Machine Learning. In Procedia Engineering (Vol. 171, pp. 1250 – 1255). Elsevier BV. https://doi.org/10.1016/j.proeng.2017.01.418 Kim, I.-H., Jeon, H., Baek, S.-C., Hong, W.-H., & Jung, H.-J. (2018). Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle. In Sensors (Vol. 18, Issue 6, p. 1881). MDPI AG. https://doi.org/10.3390/s18061881 Silva, W. R. L. da, & Lucena, D. S. de. (2018). Concrete Cracks Detection Based on Deep Learning Image Classification. In The 18th International Conference on Experimental Mechanics. The International Conference on Experimental Mechanics. MDPI. https://doi.org/10.3390/icem18 05387 Eslami E, Yun HB (2021) Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images. Sensors 21(15):5137. https://doi.org/10.3390/S21155137 Yoon, S., Spencer, B. F., Jr., Lee, S., Jung, H., & Kim, I. (2022). A novel approach to assess the seismic performance of deteriorated bridge structures by employing UAV‐based damage detection. In Structural Control and Health Moni toring (Vol. 29, Issue 7). Hindawi Limited. https://doi.org/10.1002/stc.2964 Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation (Version 1). arXiv. https://doi.org/10.48550/ARXIV.1505.04597 Agarap, A. F. (2018). Deep Learning using Rectified Linear Units (ReLU) (Version 2). arXiv. https://doi.org/10.48550/ARXIV.1803.08375 Kulkarni, S., Singh, S., Balakrishnan, D., Sharma, S., Devunuri, S., & Korlapati, S. C. R. (2023). CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks. In Lecture Notes in Computer Science (pp. 179 – 195). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-25082-8_12 Shi, Y., Cui, L., Qi, Z., Meng, F., & Chen, Z. (2016). Automatic Road Crack Detection Using Random Structured Forests. In IEEE Transactions on Intelligent Transportation Systems (Vol. 17, Issue 12, pp. 3434 – 3445). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tits.2016.2552248 Cui, L., Qi, Z., Chen, Z., Meng, F., & Shi, Y. (2015). Pavement Distress Detection Using Random Decision Forests. In Data Science (pp. 95 – 102). Springer International Publishing. https://doi.org/10.1007/978-3-319-24474-7_14

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