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Detection of damage locations and damage steps in pile foundations using acoustic emissions with deep learning technology

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Abstract

The aim of this study is to propose a new detection method for determining the damage locations in pile foundations based on deep learning using acoustic emission data. First, the damage location is simulated using a back propagation neural network deep learning model with an acoustic emission data set acquired from pile hit experiments. In particular, the damage location is identified using two parameters: the pile location (PL) and the distance from the pile cap (DS). This study investigates the influences of various acoustic emission parameters, numbers of sensors, sensor installation locations, and the time difference on the prediction accuracy of PL and DS. In addition, correlations between the damage location and acoustic emission parameters are investigated. Second, the damage step condition is determined using a classification model with an acoustic emission data set acquired from uniaxial compressive strength experiments. Finally, a new damage detection and evaluation method for pile foundations is proposed. This new method is capable of continuously detecting and evaluating the damage of pile foundations in service.

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References

  1. Mao W, Aoyama S, Goto S, Towhata I. Acoustic emission characteristics of subsoil subjected to vertical pile loading in sand. Journal of Applied Geophysics, 2015, 119: 119–127

    Article  Google Scholar 

  2. Mao W W, Aoyama S, Towhata I. Feasibility study of using acoustic emission signals for investigation of pile spacing effect on group pile behavior. Applied Acoustics, 2018, 139: 189–202

    Article  Google Scholar 

  3. Mao W W, Towhata I, Aoyama S, Goto S. Grain crushing under pile tip explored by acoustic emission. Geotechnical Engineering, 2016, 47(4): 164–175

    Google Scholar 

  4. Mao W W, Aoyama S, Goto S, Towhata I. Behaviour and frequency characteristics of acoustic emissions from sandy ground under model pile penetration. Near Surface Geophysics, 2016, 14(6): 515–525

    Article  Google Scholar 

  5. Shehadeh M F, Elbatran A H, Mehanna A, Steel J A, Reuben R L. Evaluation of acoustic emission source location in long steel pipes for continuous and semi-continuous sources. Journal of Nondestructive Evaluation, 2019, 38(2): 40

    Article  Google Scholar 

  6. Mao W, Yang Y, Lin W. An acoustic emission characterization of the failure process of shallow foundation resting on sandy soils. Ultrasonics, 2019, 93: 107–111

    Article  Google Scholar 

  7. Madarshahian R, Ziehl P, Caicedo J M. Acoustic emission Bayesian source location: Onset time challenge. Mechanical Systems and Signal Processing, 2019, 123: 483–495

    Article  Google Scholar 

  8. Ohtsu M. The history and development of acoustic emission in concrete engineering. Magazine of Concrete Research, 1996, 48(177): 321–330

    Article  Google Scholar 

  9. Grosse C U. Acoustic emission (AE) evaluation of reinforced concrete structures. In: Maierhofer C, Reinhardt H-W, Dobmann G, eds. Non-Destructive Evaluation of Reinforced Concrete Structures. Oxford: Woodhead Publishing, 2010, 185–214

    Chapter  Google Scholar 

  10. Li X Y, Li J L, Qu Y Z, He D. Gear pitting fault diagnosis using integrated CNN and GRU network with both vibration and acoustic emission signals. Applied Sciences-Basel, 2019, 9(4): 768

    Article  Google Scholar 

  11. He M, He D. Deep learning based approach for bearing fault diagnosis. IEEE Transactions on Industry Applications, 2017, 53(3): 3057–3065

    Article  Google Scholar 

  12. Guo W C, Li B Z, Shen S G, Zhou Q Z. An intelligent grinding burn detection system based on two-stage feature selection and stacked sparse autoencoder. International Journal of Advanced Manufacturing Technology, 2019, 103(5–8): 2837–2847

    Article  Google Scholar 

  13. Sohaib M, Kim J M. Data driven leakage detection and classification of a boiler tube. Applied Sciences-Basel, 2019, 9(12): 2450

    Article  Google Scholar 

  14. Carter A, Briens L. An application of deep learning to detect process upset during pharmaceutical manufacturing using passive acoustic emissions. International Journal of Pharmaceutics, 2018, 552(1–2): 235–240

    Article  Google Scholar 

  15. Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790

    Article  MathSciNet  Google Scholar 

  16. Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59(2): 433–456

    Article  Google Scholar 

  17. Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial Neural Network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359

    Article  Google Scholar 

  18. Ebrahimkhanlou A, Dubuc B, Salamone S. A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels. Mechanical Systems and Signal Processing, 2019, 130: 248–272

    Article  Google Scholar 

  19. Ebrahimkhanlou A, Salamone S. Single-sensor acoustic emission source localization in plate-like structures using deep learning. Aerospace (Basel, Switzerland), 2018, 5(2): 50

    Google Scholar 

  20. Jiao Y, Zhang Y, Shan W, Han Q, Zhao Y, Liu S. Damage fracture characterization of reinforced concrete beam subjected to four-point bending with parametric analysis of static, dynamic, and acoustic properties. Structural Health Monitoring, 2020, 19(4): 1202–1218

    Article  Google Scholar 

  21. Logoń D. Identification of the destruction process in quasi brittle concrete with dispersed fibers based on acoustic emission and sound spectrum. Materials (Basel), 2019, 12(14): 2266

    Article  Google Scholar 

  22. Xu Y, Wei S, Bao Y, Li H. Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network. Structural Control and Health Monitoring, 2019, 26(3): e2313

    Article  Google Scholar 

  23. Jang K, Kim N, An Y K. Deep learning-based autonomous concrete crack evaluation through hybrid image scanning. Structural Health Monitoring, 2019, 18(5–6): 1722–1737

    Article  Google Scholar 

  24. Silva M, Santos A, Santos R, Figueiredo E, Sales C, Costa J C W A. Deep principal component analysis: An enhanced approach for structural damage identification. Structural Health Monitoring, 2018, 18(5–6): 1444–1463

    Google Scholar 

  25. Liu Y, Zhu J J, Roberts N, Chen K M, Yan Y L, Mo S R, Gu P, Xing H Y. Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks. Nuclear Science and Techniques, 2019, 30(10): 148

    Article  Google Scholar 

  26. Tao H, Liao X, Zhao D, Gong X, Cassidy D P. Delineation of soil contaminant plumes at a co-contaminated site using BP neural networks and geostatistics. Geoderma, 2019, 354: 113878

    Article  Google Scholar 

  27. Zhu C, Zhang J, Liu Y, Ma D, Li M, Xiang B. Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: A case study in Sichuan, China. Natural Hazards, 2020, 100(1): 173–204

    Article  Google Scholar 

  28. Kim C H, Kim Y C. Application of Artificial Neural Network over nickel-based catalyst for Combined Steam-Carbon Dioxide of Methane Reforming (CSDRM). Journal of Nanoscience and Nanotechnology, 2020, 20(9): 5716–5719

    Article  Google Scholar 

  29. Zhou Z, Zhou J, Dong L, Cai X, Rui Y, Ke C. Experimental study on the location of an acoustic emission source considering refraction in different media. Scientific Reports, 2017, 7(1): 7472

    Article  Google Scholar 

  30. Mu W L, Zou Z X, Sun H L, Liu G J, Wang S J. Research on the time difference of arrival location method of an acoustic emission source based on visible graph modelling. Insight (American Society of Ophthalmic Registered Nurses), 2018, 60(12): 697–701

    Google Scholar 

  31. Lee J W, Kim H, Oh T M. Acoustic emission characteristics during uniaxial compressive loading for concrete specimens according to sand content ratio. KSCE Journal of Civil Engineering, 2020, 24(9): 2808–2823

    Article  Google Scholar 

  32. Aydemir E, Tuncer T, Dogan S. A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method. Medical Hypotheses, 2020, 134: 109519

    Article  Google Scholar 

  33. Ayyıldız H, Arslan Tuncer S. Determination of the effect of red blood cell parameters in the discrimination of iron deficiency anemia and beta thalassemia via Neighborhood Component Analysis Feature Selection-Based machine learning. Chemometrics and Intelligent Laboratory Systems, 2020, 196: 103886

    Article  Google Scholar 

  34. Bonah E, Huang X, Yi R, Aheto J H, Osae R, Golly M. Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm. Journal of Food Process Engineering, 2019, 42(6): e13236

    Article  Google Scholar 

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Acknowledgements

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. NRF-2019R1G1A1100517), the Fundamental Research Funds for the Central Universities (N170108029), the National Natural Science Foundation of China (Grant Nos. U1602232 and 51474050), and China Government Scholarship (201806080061); all of the above-mentioned funding sources and kind help are gratefully acknowledged.

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Authors and Affiliations

  1. School of Resource & Civil Engineering, Northeastern University, Shenyang, 110819, China

    Alipujiang Jierula & Shuhong Wang

  2. School of Mechanical Engineering, Pusan National University, Busan, 46241, Korea

    Alipujiang Jierula & Joon-Hyun Lee

  3. Department of Civil and Environmental Engineering, Pusan National University, Busan, 46241, Korea

    Tae-Min Oh & Jong-Won Lee

  4. Korea Institute of Geoscience and Mineral Resource (KIGAM), Daejeon, 34132, Korea

    Hyunwoo Kim

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  1. Alipujiang Jierula
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  2. Tae-Min Oh
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  4. Joon-Hyun Lee
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  5. Hyunwoo Kim
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  6. Jong-Won Lee
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Correspondence to Tae-Min Oh.

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Jierula, A., Oh, TM., Wang, S. et al. Detection of damage locations and damage steps in pile foundations using acoustic emissions with deep learning technology. Front. Struct. Civ. Eng. 15, 318–332 (2021). https://doi.org/10.1007/s11709-021-0715-y

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  • DOI: https://doi.org/10.1007/s11709-021-0715-y

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