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

doi:10.1007/s11709-023-0975-9

Front. Struct. Civ. Eng.

2023, Vol. 17

Issue (8) : 1281-1294 https://doi.org/10.1007/s11709-023-0975-9

RESEARCH ARTICLE

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

Yantao ZHU^1,^2,³(

), Qiangqiang JIA^3,⁴, Kang ZHANG^1,², Yangtao LI^1,², Zhipeng LI^1,², Haoran WANG^1,²

¹. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
². College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
³. National Dam Safety Research Center, Wuhan 430010, China
⁴. Changjiang Institute of Survey, Planning, Design, and Research, Wuhan 430010, China

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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.

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

Corresponding Author(s): Yantao ZHU

About author:

Peng Lei and Charity Ngina Mwangi contributed equally to this work.

Just Accepted Date: 12 May 2023 Online First Date: 18 October 2023 Issue Date: 16 November 2023

Cite this article:

Yantao ZHU,Qiangqiang JIA,Kang ZHANG, et al. Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning technology[J]. Front. Struct. Civ. Eng., 2023, 17(8): 1281-1294.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-023-0975-9
https://academic.hep.com.cn/fsce/EN/Y2023/V17/I8/1281

Fig.1 Stress analysis of the structure: (a) global stress analysis; (b) microsegmental stress analysis.

Fig.2 Schematic for calculating curvature modes from strain modes.

Fig.3 Displacement modal curve of the structure.

Fig.4 Topology of BP neural network.

Fig.5 Flowchart of structural damage assessment modeling.

Fig.6 Finite element model of the gravity dam.

Tab.1 Curvature mode factor of gravity dam with 12% single damage ( × 10^?3)

modal factor	calculated value
$1 / 1 Δ ω 1 Δ ω 1$	90.91
$1 / 1 Δ ω 2 Δ ω 2$	70.42
$1 / 1 Δ ω 3 Δ ω 3$	45.45
$ω 1 / ω 1 Δ ω 1 Δ ω 1$	399.38
$ω 2 / ω 2 Δ ω 2 Δ ω 2$	701.54
$ω 3 / ω 3 Δ ω 3 Δ ω 3$	545.32

Tab.2 Frequency modal factor of gravity dam with 12% single damage

Fig.7 Schematic diagram of single-point damage position of the gravity dam.

Tab.3 Single-point damage assessment results of the gravity dam

Tab.4 Curvature mode factor for gravity dams with multiple point damage of 12% ( × 10^?3)

Fig.8 Schematic diagram of multipoint damage position of the gravity dam.

modal factor	calculated value
$1 / 1 Δ ω 1 Δ ω 1$	42.37
$1 / 1 Δ ω 2 Δ ω 2$	43.48
$1 / 1 Δ ω 3 Δ ω 3$	15.38
$ω 1 / ω 1 Δ ω 1 Δ ω 1$	195.24
$ω 2 / ω 2 Δ ω 2 Δ ω 2$	454.26
$ω 3 / ω 3 Δ ω 3 Δ ω 3$	193.55

Tab.5 Frequency modal factor for gravity dams with multipoint damage of 12% ( × 10^?3)

Tab.6 Multipoint damage assessment results of the gravity dam

Tab.7 Evaluation and comparison of damage identification results

1	D Zhuang, K Ma, C Tang, X Cui, G Yang. 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 https://doi.org/10.1016/j.ijrmms.2018.11.016
2	Y Li, T Bao, X Shu, Z Gao, J Gong, K Zhang. 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 https://doi.org/10.1007/s13349-021-00520-w
3	B S Wang, Z C He. 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 https://doi.org/10.1016/j.jsv.2007.01.008
4	H Kim, E Ahn, M Shin, S H Sim. Crack and noncrack classification from concrete surface images using machine learning. Structural Health Nonitoring, 2019, 18(3): 725–738 https://doi.org/10.1177/1475921718768747
5	H Su, J Li, Z Wen, Z Guo, R Zhou. Integrated certainty and uncertainty evaluation approach for seepage control effectiveness of a gravity dam. Applied Mathematical Modelling, 2019, 65: 1–22 https://doi.org/10.1016/j.apm.2018.07.004
6	L Yang, H Su, Z Wen. Improved PLS and PSO methods-based back analysis for elastic modulus of dam. Advances in Engineering Software, 2019, 131: 205–216 https://doi.org/10.1016/j.advengsoft.2019.02.005
7	H Su, J Hu, H Li. Multi-scale performance simulation and effect analysis for hydraulic concrete submitted to leaching and frost. Engineering with Computers, 2018, 34(4): 821–842 https://doi.org/10.1007/s00366-018-0575-9
8	Y Zhu, X Niu, J Wang, C Gu, E Zhao, L Huang. Inverse analysis of the partitioning deformation modulusof high-arch dams based on quantum genetic algorithm. Advances in Civil Engineering, 2020, 2020: 1–12 https://doi.org/10.1155/2020/9842140
9	Y Zhu, X Niu, C Gu, B Dai, L Huang. A fuzzy clustering logic life loss risk evaluation model for dam-break floods. Complexity, 2021, 2021: 1–14 https://doi.org/10.1155/2021/7093256
10	M Saadatmorad, R A J Talookolaei, M H Pashaei, S Khatir, M A Wahab. Pearson correlation and discrete wavelet transform for crack identification in steel beams. Mathematics, 2022, 10(15): 2689 https://doi.org/10.3390/math10152689
11	T Bao, J Li, J Zhao. Study of quantitative crack monitoring and POF layout of concrete dam based on POF-OTDR. Scientia Sinica Technologica, 2019, 49(3): 343–350 https://doi.org/10.1360/N092017-00350
12	L V Ho, T T Trinh, G De Roeck, T Bui-Tien, L Nguyen-Ngoc, M Abdel Wahab. An efficient stochastic-based coupled model for damage identification in plate structures. Engineering Failure Analysis, 2022, 131: 105866 https://doi.org/10.1016/j.engfailanal.2021.105866
13	B SevimA C AltuniiikA Bayraktar. Earthquake behavior of berke arch dam using ambient vibration test results. Journal of Performance of Constructed Facilities, 2012, 26(6): 780−792
14	D Yuan, C Gu, X Qin, C Shao, J He. Performance-improved TSVR-based DHM model of super high arch dams using measured air temperature. Engineering Structures, 2022, 250: 113400 https://doi.org/10.1016/j.engstruct.2021.113400
15	T Bao, J Li, Y Lu, C Gu. IDE-MLSSVR-based back analysis method for multiple mechanical parameters of concrete dams. Journal of Structural Engineering, 2020, 146(8): 04020155 https://doi.org/10.1061/(ASCE)ST.1943-541X.0002602
16	J J Koenderink, A J Van Doorn. Surface shape and curvature scales. Image and Vision Computing, 1992, 10(8): 557–564 https://doi.org/10.1016/0262-8856(92)90076-F
17	A Jierula, T M Oh, S Wang, J H Lee, H Kim, J W Lee. 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 https://doi.org/10.1007/s11709-021-0715-y
18	B Chiaia, G Marasco, S Aiello. Deep convolutional neural network for multi-level non-invasive tunnel lining assessment. Frontiers of Structural and Civil Engineering, 2022, 16(2): 214–223 https://doi.org/10.1007/s11709-021-0800-2
19	P Savino, F Tondolo. Automated classification of civil structure defects based on convolutional neural network. Frontiers of Structural and Civil Engineering, 2021, 15(2): 305–317 https://doi.org/10.1007/s11709-021-0725-9
20	H Federer. Curvature measures. Transactions of the American Mathematical Society, 1959, 93(3): 418–491 https://doi.org/10.1090/S0002-9947-1959-0110078-1
21	S S Wang, Q W Ren. Dynamic response of gravity dam model with crack and damage detection. Science China Technological Sciences, 2011, 54(3): 541–546 https://doi.org/10.1007/s11431-010-4226-7
22	D Feng, M Q Feng. Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection—Review. Engineering Structures, 2018, 156: 105–117 https://doi.org/10.1016/j.engstruct.2017.11.018
23	F Al Thobiani, S Khatir, B Benaissa, E Ghandourah, S Mirjalili, M Abdel Wahab. A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification. Theoretical and Applied Fracture Mechanics, 2022, 118: 103213 https://doi.org/10.1016/j.tafmec.2021.103213
24	L V Ho, D H Nguyen, M Mousavi, G De Roeck, T Bui-Tien, A H Gandomi, M A Wahab. A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks. Computers & Structures, 2021, 252: 106568 https://doi.org/10.1016/j.compstruc.2021.106568
25	X Li, X Chen, A P Jivkov, J Hu. 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 https://doi.org/10.1002/stc.2909
26	US ShanthamalluA Spanias. Neural Networks and Deep Learning. Determination Press, 2022, 43–57

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