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Architecture Monitoring and Reliability Estimation Based on DIP Technology

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12855))

Abstract

All civil infrastructure units demand regular inspection to avoid functional and structural damages. Periodic examinations are in accordance with classification society’s standards which contain both non-destructive tests and visual surveys, to search structural damage, reliability, cracks, thickness measurement, and Water dripping generally documented by manually or with measurements tape. But, it is very hard to search cracks by visually monitoring much larger structures. Hence, the advent of crack detecting and monitoring systems has been a major issue. In this proposed study a crack detection algorithm in reference to digital image processing technology is suggested. Obtaining information of a surface crack by using an image pre-processing pipeline and to estimate the failure is proposed which helps and detects the structure cracks and body health information. It provides the identification system more portable and integrated, estimates the crack more precisely and reduction in expenditure as well. The proposed algorithm accuracy is 93.8% as compared to the traditional and recent work.

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References

  1. Chen, S., Laefer, D.F., Mangina, E., Zolanvari, S.I., Byrne, J.: UAV bridge inspection through evaluated 3D reconstructions. J. Bridge Eng. 24(4), 05019001 (2019)

    Article  Google Scholar 

  2. Guédé, F.: Risk-based structural integrity management for offshore jacket platforms. Mar. Struct. 63, 444–461 (2019)

    Article  Google Scholar 

  3. Lv, Y., et al.: Quality control of the continuous hot pressing process of medium density fiberboard using fuzzy failure mode and effects analysis. Appl. Sci. 10(13), 4627 (2020)

    Article  Google Scholar 

  4. Urbonas, A., Raudonis, V., Maskeliūnas, R., Damaševičius, R.: Automated identification of wood veneer surface defects using faster region-based convolutional neural network with data augmentation and transfer learning. Appl. Sci. 9(22), 4898 (2019)

    Google Scholar 

  5. Capizzi, G., Lo Sciuto, G., Woźniak, M., Damaševicius, R.: A clustering based system for automated oil spill detection by satellite remote sensing. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 613–623. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39384-1_54

    Chapter  Google Scholar 

  6. Grycuk, R., Wojciechowski, A., Wei, W., Siwocha, A.: Detecting visual objects by edge crawling. J. Artif. Intell. Soft Comput. Res. 10(3), 223–237 (2020)

    Article  Google Scholar 

  7. Grycuk, R., Najgebauer, P., Kordos, M., Scherer, M.M., Marchlewska, A.: Fast image index for database management engines. J. Artif. Intell. Soft Comput. Res. 10(2), 113–123 (2020)

    Article  Google Scholar 

  8. Guo, L., Woźniak, M.: An image super-resolution reconstruction method with single frame character based on wavelet neural network in internet of things. Mobile Netw. Appl. 26, 1–14 (2020)

    Google Scholar 

  9. Korytkowski, M., Scherer, R., Szajerman, D., Połap, D., Woźniak, M.: Efficient visual classification by fuzzy rules. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), July 2020

    Google Scholar 

  10. Ma, Z., Liu, S.: A review of 3D reconstruction techniques in civil engineering and their applications. Adv. Eng. Inform. 37, 163–174 (2018)

    Article  Google Scholar 

  11. Mubashshira, S., Azam, M.M., Ahsan, S.M.M.: An unsupervised approach for road surface crack detection. In: 2020 IEEE Region 10 Symposium (TENSYMP), pp. 1596–1599 (2020)

    Google Scholar 

  12. Połap, D., Woźniak, M. Bacteria shape classification by the use of region covariance and convolutional neural network. In: 2019 International Joint Conference on Neural Networks (IJCNN), July 2019

    Google Scholar 

  13. Shifani, S.A., Thulasiram, P., Narendran, K., Sanjay, D.R.: A study of methods using image processing technique in crack detection. In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 578–582 (2020)

    Google Scholar 

  14. Wang, G., Liu, Y., Xiang, J.: A two-stage algorithm of railway sleeper crack detection based on edge detection and CNN. In: 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), pp. 1–5 (2020)

    Google Scholar 

  15. Kumar, B., Ghosh, S.: Detection of concrete cracks using dual-channel deep convolutional network. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7 (2020)

    Google Scholar 

  16. Wang, L., Ye, Y.: Computer vision-based Road Crack Detection Using an Improved I-UNet convolutional networks. In: 2020 Chinese Control and Decision Conference (CCDC), pp. 539–543 (2020)

    Google Scholar 

  17. Yang, F.: Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 21(4), 1525–1535 (2020)

    Article  Google Scholar 

  18. XingQi, G., Quan, L., MeiLing, Z., HuiFeng, J.: Analysis and test of concrete surface crack of railway bridge based on deep learning. In: 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 437–442 (2020)

    Google Scholar 

  19. Sundararajan, D.: Edge detection. In: Digital Image Processing, pp. 257–280. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6113-4_9

    Chapter  MATH  Google Scholar 

  20. Yuhan, Z., Juan, Q., Zhiling, G., Kuncheng, J., Shiyuan, C.: Detection of road surface crack based on PYNQ. In: 2020 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China, pp. 1150–1154 (2020)

    Google Scholar 

  21. Ahmad, A.R., Osman, M.K., Ahmad, K.A., Anuar, M.A., Yusof, N.A.M.: Image segmentation for pavement crack detection system. In: 2020 10th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, pp. 153–157 (2020)

    Google Scholar 

  22. Woźniak, M., Wieczorek, M., Siłka, J., Połap, D.: Body pose prediction based on motion sensor data and recurrent neural network. IEEE Trans. Ind. Inform. 17(3), 2101–2111 (2020)

    Article  Google Scholar 

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Correspondence to Robertas Damasevicius .

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Shah, F.M. et al. (2021). Architecture Monitoring and Reliability Estimation Based on DIP Technology. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12855. Springer, Cham. https://doi.org/10.1007/978-3-030-87897-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-87897-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87896-2

  • Online ISBN: 978-3-030-87897-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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