Full-scale study on the mechanism of flexural damage evolution of in-service prestressed concrete box girders

Abstract

Prestressed concrete box girders are widely used in bridge engineering. Effective monitoring to accurately detect, localize, and evaluate damage development in in-service box girders is essential to ensure their operational safety. In this study, a four-point bending test based on acoustic emission (AE) technology was conducted on a full-scale in-service box girder with 20 years of service. The correlation between damage evolution and AE parameters was investigated, laying the foundation for the application of AE technology in bridge monitoring. The results show that the cracking load of the box girder after 20 years of service is lower than the theoretical design value. Changes in the cumulative AE ring counts can characterize different failure stages of the box girder. AE signals were grouped by amplitude and peak frequency for analysis, and the effectiveness of different AE signals in tracking damage development was examined. The Gaussian mixture model (GMM) clustering algorithm was used to perform the clustering analysis of rise time/peak amplitude (RA) and ring-down counts/duration (AF). This method can effectively distinguish microcrack types and identify failure modes in different damage stages. In conclusion, AE monitoring can characterize the structural damage state and improve the safety and reliability of box girders.

Keywords

prestressed concrete box girder / full-scale experiment / acoustic emission / Gaussian mixture model / damage evolution

References

[1] HUANG J Y, GAO L L, LIU Z F, et al. Research on improving the bearing capacity of full-scale PC small box girder[J/OL]. Journal of China & Foreign Highway, 2025[2025-04-25]. https://link.cnki.net/urlid/43.1363.u.20250425.1120.003. (in Chinese)

[2] JIA J W, NI Y H, MAO J X, et al. Geometric parameter identification of bridge precast box girder sections based on deep learning and computer vision[J]. Journal of Southeast University (English Edition), 2025, 41(3): 278-285.

[3] MA R J, XU S Q, WANG D L, et al. Fatigue life analysis of steel box girder of long-span suspension bridge based on big data[J]. Journal of South China University of Technology (Natural Science Edition), 2017, 45(6): 66-73. (in Chinese)

[4] CHEN C, WANG Z L, GAO Q F. A reliability assessment method for concrete box-girder bridges[J]. Journal of Harbin Institute of Technology, 2017, 49(3): 113-119. (in Chinese)

[5] SHENOY C V, FRANTZ G C. Structural tests of 27-year-old prestressed concrete bridge beams[J]. PCI Journal, 1991, 36(6): 80-90.

[6] HARRIES K A. Structural testing of prestressed concrete girders from the lake view drive bridge[J]. Journal of Bridge Engineering, 2009, 14(2): 78-92.

[7] ZHAO Y, HE S H, LI C F, et al. Bearing capacity assessment of in-service prestressed concrete box girders after cracking[J]. Journal of Tongji University (Natural Science), 2010, 38(9): 1271-1275. (in Chinese)

[8] XU X F, ZHANG F, WEI C L. Stiffness damage assessment of prestressed concrete box-girder after cracking[J]. Engineering Mechanics, 2015, 32(7): 95-102. (in Chinese)

[9] YUAN A M, DAI H, SUN D S, et al. Behaviors of segmental concrete box beams with internal tendons and external tendons under bending[J]. Engineering Structures, 2013, 48: 623-634.

[10] ELBATANOUNY E, HENDERSON A, AI L, et al. Condition assessment of prestressed concrete channel bridge girders using acoustic emission and data-driven methods[J]. Structures, 2024, 67: 107008.

[11] NIE Q P, LIU H N, MA T F, et al. Experimental study on acoustic emission of steel fiber broken pebble recycled concrete[J]. Construction and Building Materials, 2024, 452: 138942.

[12] ZHAO Y F, CHEN M H, JIANG X, et al. Damage evolution and avalanche characteristics of concrete under salt-freezing action by acoustic emission[J]. Developments in the Built Environment, 2025, 21: 100600.

[13] BRAYDI A, FOSSAT P, ARDABILIAN M, et al. Innovative prognostic methodology for pipe defect detection leveraging acoustic emissions analysis and computational modeling integration[J]. Applied Ocean Research, 2025, 158: 104521.

[14] SAGAR R V, RAGHU PRASAD B K, NAZREEN S, et al. Modeling mode-I fracture process in concrete at meso-scale: Computational aspects of lattice model and a comparison between results of two dimensional lattice simulation and acoustic emission measurements[J]. Engineering Fracture Mechanics, 2019, 210: 257-278.

[15] LIU K, WULAN T Y, YAO Y D, et al. Assessment of damage evolution of concrete beams strengthened with BFRP sheets with acoustic emission and unsupervised machine learning[J]. Engineering Structures, 2024, 300: 117228.

[16] Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Standard for test methods of physical and mechanical properties of concrete: GB/T 50081—2019[S]. Beijing: China Architecture & Building Press, 2019. (in Chinese)

[17] General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China. Metallic materials—Tensile testing—Part 1: Method of test at room temperature: GB/T 228.1—2010[S]. Beijing: China Architecture & Building Press, 2010. (in Chinese)

[18] PREM P R, VERMA M, AMBILY P S. Damage characterization of reinforced concrete beams under different failure modes using acoustic emission[J]. Structures, 2021, 30: 174-187.

[19] LI S L, CHEN S H, LI M, et al. Separation method for acoustic emission signals of prestressing steel strand fracture and concrete cracking[J]. Measurement, 2025, 245: 116607.

[20] XU J M, WANG K, MA Q T, et al. Study on acoustic emission properties and crack growth rate identification of rail steels under different fatigue loading conditions[J]. International Journal of Fatigue, 2023, 172: 107638.

[21] WANG Z X, CHEN X D, LENG Y, et al. Mechanical properties and damage evolution of sprayed ultrahigh performance concrete under uniaxial compression[J]. Journal of Southeast University (English Edition), 2025, 41(2): 171-179.

[22] DAS A K, SUTHAR D, LEUNG C K Y. Machine learning based crack mode classification from unlabeled acoustic emission waveform features[J]. Cement and Concrete Research, 2019, 121: 42-57.

Funding

National Key Research and Development Program Young Scientist Project(2024YFB3714900)

National Natural Science Foundation of China(52379124)