Open Access
| Issue |
MATEC Web Conf.
Volume 413, 2025
International Conference on Measurement, AI, Quality and Sustainability (MAIQS 2025)
|
|
|---|---|---|
| Article Number | 05004 | |
| Number of page(s) | 5 | |
| Section | Artificial Intelligence for NDT | |
| DOI | https://doi.org/10.1051/matecconf/202541305004 | |
| Published online | 01 October 2025 | |
- Cecilia L. Wilson, Kuldeep Lonkar, Surajit Roy, Fotis Kopsaftopoulos, Fu-Kuo Chang, Structural Health Monitoring of Composites, Comprehensive Composite Materials II, (Elsevier, 2018) [Google Scholar]
- Aeshah H. Alamri, Localized corrosion and mitigation approach of steel materials used in oil and gas pipelines, An overview. Engineering Failure Analysis (Elsevier, 2020) [Google Scholar]
- G.S. Frankel, N. Sridhar, Understanding localized corrosion (Materials Today, Elsevier, 2008) [Google Scholar]
- Pavan, G., Galati, G., & Daum F., Lessons Learnt from the Rise and Fall of Quantum Radar Research (Academia Quantum 2025) [Google Scholar]
- Sandeep Kumar Dwivedi, Manish Vishwakarma, Prof. Akhilesh Soni, Advances and Researches on Non-Destructive Testing: A Review, Materials Today: Proceedings, Volume 5, pp. 3690-3698, (2018) https://doi.org/10.1016/j.matpr.2017.11.620 [Google Scholar]
- Anton Du Plessis, Eric MacDonald, Jess M. Waller, Filippo Berto, Non-destructive testing of parts produced by laser powder bed fusion, In Additive Manufacturing Materials and Technologies, Fundamentals of Laser Powder Bed Fusion of Metals (Elsevier 2021) [Google Scholar]
- Mohamed S. Kaseko, Stephen G. Ritchie, A neural network-based methodology for pavement crack detection and classification, Transportation Research Part C: Emerging Technologies (Elsevier 1993) [Google Scholar]
- Ikhlas Abdel-Qader, Sara Pashaie-Rad, Osama Abudayyeh, Sherif Yehia, PCA-Based algorithm for unsupervised bridge crack detection, Advances in Engineering Software (Elsevier 2006) [Google Scholar]
- Shi, Yong & Cui, Limeng & Qi, Zhiquan & Meng, Fan & Chen, Zhensong. Automatic Road Crack Detection Using Random Structured Forests. IEEE Transactions on Intelligent Transportation Systems. [Google Scholar]
- (2016). DOI:10.1109/TITS.2016.2552248A [Google Scholar]
- L. Zhang, F. Yang, Y. Daniel Zhang and Y. J. Zhu, "Road crack detection using deep convolutional neural network," 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 2016, pp. 3708-3712 [Google Scholar]
- Freund, Y., Schapire, R., and Abe, N. A short introduction to boosting. Journal-Japanese Soc. Artif. Intell. 14, 1612 (1999) [Google Scholar]
- Cha, Y.-J., Choi, W., and Büyüköztürk, O. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civ. Infrastructure Eng. (Wiley 2017) [Google Scholar]
- Yahui Liu, Jian Yao, Xiaohu Lu, Renping Xie, Li Li, DeepCrack: A deep hierarchical feature learning architecture for crack segmentation, Neurocomputing (Elsevier 2019) [Google Scholar]
- F.-C. Chen and M. R. Jahanshahi, NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion, in IEEE Transactions on Industrial Electronics, vol. 65, pp. 4392-4400 (2018) [Google Scholar]
- Kasthurirangan Gopalakrishnan, Siddhartha K. Khaitan, Alok Choudhary, Ankit Agrawal, Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection, Construction and Building Materials (ScienceDirect 2017) [Google Scholar]
- Gao, Y., & Mosalam, K. M. (2020). PEER Hub ImageNet: A Large-Scale Multiattribute Benchmark Data Set of Structural Images. Journal of Structural Engineering, 146(10), 04020198. (2020). doi:10.1061/(asce)st.1943-541x.0002745 [Google Scholar]
- S. Ren, K. He, R. Girshick and J. Sun, "Faster R- CNN: Towards Real-Time Object Detection with Region Proposal Networks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 1 June 2017. doi:10.1109/TPAMI.2016.2577031. [Google Scholar]
- Cha, Y.-J., Choi, W., Suh, G., Mahmoudkhani, S., and Büyüköztürk, O. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civ. Infrastructure Eng. 33, 731–747. (2018) doi:10.1111/mice.12334 [Google Scholar]
- Shaikh, Zakir & Ramadass, Suguna. Unveiling deep learning powers: LSTM, BiLSTM, GRU, BiGRU, RNN comparison. Indonesian Journal of Electrical Engineering and Computer Science. 35. 263 (2024) [Google Scholar]
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