Skip to main content
SpringerLink
Log in

Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning technology

  • Research Article
  • Published:
Frontiers of Structural and Civil Engineering Aims and scope Submit manuscript

Abstract

Concrete is widely used in various large construction projects owing to its high durability, compressive strength, and plasticity. However, the tensile strength of concrete is low, and concrete cracks easily. Changes in the concrete structure will result in changes in parameters such as the frequency mode and curvature mode, which allows one to effectively locate and evaluate structural damages. In this study, the characteristics of the curvature modes in concrete structures are analyzed and a method to obtain the curvature modes based on the strain and displacement modes is proposed. Subsequently, various indices for the damage diagnosis of concrete structures based on the curvature mode are introduced. A damage assessment method for concrete structures is established using an artificial bee colony backpropagation neural network algorithm. The proposed damage assessment method for dam concrete structures comprises various modal parameters, such as curvature and frequency. The feasibility and accuracy of the model are evaluated based on a case study of a concrete gravity dam. The results show that the damage assessment model can accurately evaluate the damage degree of concrete structures with a maximum error of less than 2%, which is within the required accuracy range of damage identification and assessment for most concrete structures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhuang D, Ma K, Tang C, Cui X, Yang G. Study on crack formation and propagation in the galleries of the Dagangshan high arch dam in Southwest China based on microseismic monitoring and numerical simulation. International Journal of Rock Mechanics and Mining Sciences, 2019, 115: 157–172

    Article  Google Scholar 

  2. Li Y, Bao T, Shu X, Gao Z, Gong J, Zhang K. Data-driven crack behavior anomaly identification method for concrete dams in long-term service using offline and online change point detection. Journal of Civil Structural Health Monitoring, 2021, 11(5): 1449–1460

    Article  Google Scholar 

  3. Wang B S, He Z C. Crack detection of arch dam using statistical neural network based on the reductions of natural frequencies. Journal of Sound and Vibration, 2007, 302(4–5): 1037–1047

    Article  Google Scholar 

  4. Kim H, Ahn E, Shin M, Sim S H. Crack and noncrack classification from concrete surface images using machine learning. Structural Health Nonitoring, 2019, 18(3): 725–738

    Article  Google Scholar 

  5. Su H, Li J, Wen Z, Guo Z, Zhou R. Integrated certainty and uncertainty evaluation approach for seepage control effectiveness of a gravity dam. Applied Mathematical Modelling, 2019, 65: 1–22

    Article  MathSciNet  MATH  Google Scholar 

  6. Yang L, Su H, Wen Z. Improved PLS and PSO methods-based back analysis for elastic modulus of dam. Advances in Engineering Software, 2019, 131: 205–216

    Article  Google Scholar 

  7. Su H, Hu J, Li H. Multi-scale performance simulation and effect analysis for hydraulic concrete submitted to leaching and frost. Engineering with Computers, 2018, 34(4): 821–842

    Article  Google Scholar 

  8. Zhu Y, Niu X, Wang J, Gu C, Zhao E, Huang L. Inverse analysis of the partitioning deformation modulusof high-arch dams based on quantum genetic algorithm. Advances in Civil Engineering, 2020, 2020: 1–12

    Google Scholar 

  9. Zhu Y, Niu X, Gu C, Dai B, Huang L. A fuzzy clustering logic life loss risk evaluation model for dam-break floods. Complexity, 2021, 2021: 1–14

    Google Scholar 

  10. Saadatmorad M, Talookolaei R A J, Pashaei M H, Khatir S, Wahab M A. Pearson correlation and discrete wavelet transform for crack identification in steel beams. Mathematics, 2022, 10(15): 2689

    Article  Google Scholar 

  11. Bao T, Li J, Zhao J. Study of quantitative crack monitoring and POF layout of concrete dam based on POF-OTDR. Scientia Sinica Technologica, 2019, 49(3): 343–350

    Article  Google Scholar 

  12. Ho L V, Trinh T T, De Roeck G, Bui-Tien T, Nguyen-Ngoc L, Abdel Wahab M. An efficient stochastic-based coupled model for damage identification in plate structures. Engineering Failure Analysis, 2022, 131: 105866

    Article  Google Scholar 

  13. Sevim B, Altuniiik A C, Bayraktar A. Earthquake behavior of berke arch dam using ambient vibration test results. Journal of Performance of Constructed Facilities, 2012, 26(6): 780–792

    Article  Google Scholar 

  14. Yuan D, Gu C, Qin X, Shao C, He J. Performance-improved TSVR-based DHM model of super high arch dams using measured air temperature. Engineering Structures, 2022, 250: 113400

    Article  Google Scholar 

  15. Bao T, Li J, Lu Y, Gu C. IDE-MLSSVR-based back analysis method for multiple mechanical parameters of concrete dams. Journal of Structural Engineering, 2020, 146(8): 04020155

    Article  Google Scholar 

  16. Koenderink J J, Van Doorn A J. Surface shape and curvature scales. Image and Vision Computing, 1992, 10(8): 557–564

    Article  Google Scholar 

  17. Jierula A, Oh T M, Wang S, Lee J H, Kim H, Lee J W. Detection of damage locations and damage steps in pile foundations using acoustic emissions with deep learning technology. Frontiers of Structural and Civil Engineering, 2021, 15(2): 318–332

    Article  Google Scholar 

  18. Chiaia B, Marasco G, Aiello S. Deep convolutional neural network for multi-level non-invasive tunnel lining assessment. Frontiers of Structural and Civil Engineering, 2022, 16(2): 214–223

    Article  Google Scholar 

  19. Savino P, Tondolo F. Automated classification of civil structure defects based on convolutional neural network. Frontiers of Structural and Civil Engineering, 2021, 15(2): 305–317

    Article  Google Scholar 

  20. Federer H. Curvature measures. Transactions of the American Mathematical Society, 1959, 93(3): 418–491

    Article  MathSciNet  MATH  Google Scholar 

  21. Wang S S, Ren Q W. Dynamic response of gravity dam model with crack and damage detection. Science China Technological Sciences, 2011, 54(3): 541–546

    Article  MATH  Google Scholar 

  22. Feng D, Feng M Q. Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection—Review. Engineering Structures, 2018, 156: 105–117

    Article  Google Scholar 

  23. Al Thobiani F, Khatir S, Benaissa B, Ghandourah E, Mirjalili S, Abdel Wahab M. A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification. Theoretical and Applied Fracture Mechanics, 2022, 118: 103213

    Article  Google Scholar 

  24. Ho L V, Nguyen D H, Mousavi M, De Roeck G, Bui-Tien T, Gandomi A H, Wahab M A. A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks. Computers & Structures, 2021, 252: 106568

    Article  Google Scholar 

  25. Li X, Chen X, Jivkov A P, Hu J. Assessment of damage in hydraulic concrete by gray wolf optimization-support vector machine model and hierarchical clustering analysis of acoustic emission. Structural Control and Health Monitoring, 2022, 29(4): 1–22

    Article  Google Scholar 

  26. Shanthamallu US, Spanias A. Neural Networks and Deep Learning. Determination Press, 2022, 43–57

Download references

Acknowledgements

This study was supported by the National Key R&D Program of China (No. 2022YFC3005401), the National Natural Science Foundation of China (Grant No. 52309152), the Fundamental Research Funds for the Central Universities (B230201013), the Natural Science Foundation of Jiangsu Province (BK20220978) and China Postdoctoral Science Foundation (2022M720998).

Author information

Authors and Affiliations

  1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, 210098, China

    Yantao Zhu, Kang Zhang, Yangtao Li, Zhipeng Li & Haoran Wang

  2. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, China

    Yantao Zhu, Kang Zhang, Yangtao Li, Zhipeng Li & Haoran Wang

  3. National Dam Safety Research Center, Wuhan, 430010, China

    Yantao Zhu & Qiangqiang Jia

  4. Changjiang Institute of Survey, Planning, Design, and Research, Wuhan, 430010, China

    Qiangqiang Jia

Authors
  1. Yantao Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiangqiang Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yangtao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhipeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Haoran Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yantao Zhu.

Ethics declarations

Conflict of Interest The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, Y., Jia, Q., Zhang, K. et al. Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning technology. Front. Struct. Civ. Eng. 17, 1281–1294 (2023). https://doi.org/10.1007/s11709-023-0975-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11709-023-0975-9

Keywords

Search

Navigation