Failure mechanism of reinforced concrete beam based on multi-parameter monitoring method of acoustic-optical-thermal

Yun  Sun; Da  Song; Feng  Chen; Wenjing  Zhong

doi:10.6180/jase.202603_29(3).0017

Failure mechanism of reinforced concrete beam based on multi-parameter monitoring method of acoustic-optical-thermal

Yun Sun^1,2This email address is being protected from spambots. You need JavaScript enabled to view it., Da Song¹, Feng Chen¹, and Wenjing Zhong¹

¹School of Civil Engineering, Nanyang Institute of Technology, Nanyang 473004, China

²Henan International Joint Laboratory of Dynamics of Impact and Disaster of Engineering Structures, Nanyang Institute of Technology, Nanyang, 473004, China

Received: April 13, 2025
Accepted: June 24, 2025
Publication Date: July 11, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202603_29(3).0017

This study employed a multi-parameter approach, integrating acoustic emission (AE), digital image correlation (DIC), and infrared thermal imaging (ITI) technology to investigate the failure mechanisms of reinforced concrete beams. Six beam specimens with varying reinforcement ratios were subjected to static loading and monitored by AE, DIC, and ITI to document the real-time evolution of damage. The DIC successfully quantified the full-field strain and crack propagation, revealing distinct failure modes. The stages of crack development were further analyzed through AE energy analysis, and the ITI detected temperature gradient variation associated with crack and localized damage. The results demonstrate that DIC technology can detect cracks at an earlier stage and track their dynamic change trends. The most significant energy dissipation was observed during the failure of the under-reinforced beam. Throughout the entire beam failure process, the balanced-reinforced beam exhibited the highest temperature rise rate, whereas the under-reinforced beam showed the lowest. Multi-parameter monitoring offers synergistic advantages, validating the effectiveness of integrating acoustic, optical, and thermal data for a thorough structural health evaluation. This approach provides theoretical and technical insights for non-destructive assessment and early warning in reinforced concrete structures.

Keywords: reinforced concrete beam; failure mode; acoustic emission; digital image correlation; infrared thermal image; multi-parameter monitoring

[1] A. Sendrowicz, A. O. Myhre, S. W. Wierdak, and A. Vinogradov, (2021) “Challenges and Accomplishments in Mechanical Testing Instrumented by In Situ Techniques: Infrared Thermography, Digital Image Correlation, and Acoustic Emission" Applied Sciences 11(15): 6718. DOI: 10.3390/app11156718.
[2] N. McCormick and J. Lord, (2010) “Digital Image Cor relation" Materials Today 13(12): 52–54. DOI: 10.1016/ S1369-7021(10)70235-2.
[3] Z. Wang, F. Li, and G. Mei, (2024) “OpenMP Parallel Finite-Discrete Element Method for Modeling Excavation Support with Rockbolt and Grouting" Rock Mechanics andRockEngineering57(5):3635–3657. DOI: 10.1007/s00603-023-03746-w.
[4] Z. Wang, Q. Liu, and Y. Wang, (2021) “Thermo Mechanical FDEM Model for Thermal Cracking of Rock and Granular Materials" Powder Technology 393: 807 823. DOI: 10.1016/j.powtec.2021.08.030.
[5] L. Sun, S. Tao, X. Tang, and Q. Liu, (2021) “Simulation of the Nonplanar Three-Dimensional Thermal Cracking Using the Finite Element-Meshfree Method" Applied Mathematical Modelling 99: 106–128. DOI: 10.1016/j.apm.2021.06.011.
[6] H. Zhang, J. Yang, Y. Xu, and J. Xiao, “Study on Three-Dimensional Crack Propagation Characteristics of Expansive Soil Based on Digital Image Correlation Technology" Rock and Soil Mechanics 45(S1): 309–323. DOI: 10.16285/j.rsm.2023.1587.
[7] J. Yang, H. Zhang, and C. Lin, (2022) “Analysis of Crack Morphology Evolution Law of Expansive Soil Slope Based on Digital Image Correlation Technology" Journal of Central South University (Science and Technology) 53(01): 225–238. DOI: 10.11817/j.issn.1672 7207.2022.02.020.
[8] K. Zhang, F.-f. Qi, and Y.-l. Chen, (2020) “Deformation and Fracturing Characteristics of Fracture Network Model and Influence of Filling Based on 3D Printing and DIC Technologies" Rock and Soil Mechanics 41(8): 2555–2563. DOI: doi.org/10.16285/j.rsm.2019.6625.
[9] B. K. Agarwal and A. Sachan, (2024) “Quantification of Desiccation Cracking and Strain Localization in Lime Treated Compacted Expansive Soils Using DIA and DIC" Journal of Materials in Civil Engineering 36(2): 04023541. DOI: 10.1061/JMCEE7.MTENG-16352.
[10] J. Xu, X. Dong, Y. Fu, X. Yu, and F. Zhou, (2020) “Experimental Analysis of Process and Tensile Strength for Concrete Brazilian Splitting Test with Different Loading Boundaries by Dic Method" Chinese Journal of Theoretical and Applied Mechanics 52(3): 864–876. DOI: 10.6052/0459-1879-19-303.
[11] M. Dutton, W. A. Take, and N. A. Hoult, (2014) “Curvature Monitoring of Beams Using Digital Image Correlation" Journal of Bridge Engineering 19(3): 05013001. DOI: 10.1061/(ASCE)BE.1943-5592.0000538.
[12] K. Murali B, R. Guru Prathap V, T. Tadepalli, K. Rathish P, and L. Yerra, (2019) “Numerical and Experimental Study on Flexural Behavior of Reinforced Concrete Beams: Digital Image Correlation Approach" Computers and Concrete 24(6): 561–570. DOI: 10.12989/cac.2019.24.6.561.
[13] B. Murali Krishna, V. Guru Prathap Reddy, M. Shafee, and T. Tadepalli, (2020) “Condition Assessment of RC Beams Using Artificial Neural Networks" Structures 23: 1–12. DOI: 10.1016/j.istruc.2019.09.014.
[14] S. Bhowmik and S. Ray, (2019) “An Experimental Approach for Characterization of Fracture Process Zone in Concrete" Engineering Fracture Mechanics 211: 401–419. DOI: 10.1016/j.engfracmech.2019.02.026.
[15] R.L.RiyarandS.Bhowmik,(2024)“Effect of Reinforcement Percentage and Corrosion on Fracture Properties of Reinforced Concrete" Procedia Structural Integrity 66: 181–194. DOI: 10.1016/j.prostr.2024.11.069.
[16] T. Kundu, A. Pal, P. Roy, A. K. Datta, and P. Topdar, (2024) “Development of a Novel Real-Time AE Source Localisation Technique Using ANN for Health Monitoring of Rail Section: An Experimental Study" Structural Health Monitoring 23(1): 479–494. DOI: 10.1177/14759217231171026.
[17] K. Tao and W. Zheng, (2018) “Real-Time Damage Assessment of Hydrous Sandstone Based on Synergism of AE-CT Techniques" Engineering Failure Analysis 91: 465–480. DOI: 10.1016/j.engfailanal.2018.04.046.
[18] A. Manuello, G. Niccolini, and A. Carpinteri, (2019) “AEMonitoring of a Concrete Arch Road Tunnel: Damage Evolution and Localization" Engineering Fracture Mechanics 210: 279–287. DOI: 10.1016/j.engfracmech.2018.07.029.
[19] J. Whiting, A. Springer, and F. Sciammarella, (2018) “Real-Time Acoustic Emission Monitoring of Powder Mass Flow Rate for Directed Energy Deposition" Additive Manufacturing 23: 312–318. DOI: 10.1016/j.addma. 2018.08.015.
[20] R. Wang, Z. Chen, and T. Wan, “Design and Engineering Application of Negative Moment Zone of Simply Supported to Continuous Steel-Concrete Composite Beam Bridge" Building Structure 54(01): 41–46.
[21] F. Li, S. Zhang, and B. Yuan, (2021) “Analysis of Load Bearing Behavior of Composite Girder of Mengzhou Huanghe River Highway Bridge Erected by Full-Span Method" Bridge Construction 51(02): 78–84.
[22] V. Munoz, B. Valès, M. Perrin, M. Pastor, H. Wele mane, A. Cantarel, and M. Karama, (2016) “Damage Detection in CFRP by Coupling Acoustic Emission and Infrared Thermography" Composites Part B: Engineer ing 85: 68–75. DOI: 10.1016/j.compositesb.2015.09. 011.
[23] S. Ashraf and M. Rucka, (2023) “Microcrack Monitoring and Fracture Evolution of Polyolefin and Steel Fibre Concrete Beams Using Integrated Acoustic Emission and Digital Image Correlation Techniques" Construction and Building Materials 395: 132306. DOI: 10.1016/j.conbuildmat.2023.132306.
[24] Y. He, B. Deng, H. Wang, L. Cheng, K. Zhou, S. Cai, and F. Ciampa, (2021) “Infrared Machine Vision and Infrared Thermography with Deep Learning: A Review" Infrared Physics & Technology 116: 103754. DOI: 10.1016/j.infrared.2021.103754.
[25] G. F. Sirca Jr. and H. Adeli, (2018) “Infrared Thermography for Detecting Defects in Concrete Structures" JOUR NALOFCIVILENGINEERINGANDMANAGE MENT24(7): 508–515. DOI: 10.3846/jcem.2018.6186.
[26] J. Chen, X. Chen, H. Zhao, and H. Ji, (2021) “Experimental Research and Application of Non-Destructive Detecting Techniques for Concrete-Filled Steel Tubes Based on Infrared Thermal Imaging and Ultrasonic Method" Journal of Building Structures 42(S2): 444–453. DOI: 10.14006/j.jzjgxb.2021.S2.0052.