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

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.

Graphical abstract

Keywords

hydraulic structure / curvature mode / damage detection / artifical neural network / artificial bee colony

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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[13] SevimBAltuniiikA CBayraktarA. Earthquake behavior of berke arch dam using ambient vibration test results. Journal of Performance of Constructed Facilities, 2012, 26(6): 780−792

[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

[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

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

[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

[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

[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

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

[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

[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

[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

[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

[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

[26] ShanthamalluUSSpaniasA. Neural Networks and Deep Learning. Determination Press, 2022, 43–57

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