1. Department of Architecture and Architectural Engineering, Hankyong National University, Gyeonggi-do 17579, Republic of Korea
2. Dream Structural Engineers Co., Ltd. Gyeonggi-do 18471, Republic of Korea
3. Jeonnam CCU Center, Korea Conformity Laboratories, Jeollanam-do 59631, Republic of Korea
4. School of Architecture and Architectural Engineering, Hankyong National University, Gyeonggi 17579, Republic of Korea
5. Laboratory Center of Hankyong National University, Anseong 17579, Republic of Korea
hju@hknu.ac.kr
Show less
History+
Received
Accepted
Published Online
2025-02-09
2025-09-30
2026-01-27
PDF
(5939KB)
Abstract
In this study, the crack width and deflection relationship developed for the serviceability evaluation of reinforced concrete (RC) beams is applied to the experimental results of RC beams to evaluate its applicability and limitations. In addition, digital image correlation (DIC) method and three-dimensional (3D) scanning technology are applied to the deformation measurement of RC beam experiments, and the results of each measurement were compared. This study aims to provide a basis for expanding the applicability of reliable digital image-based measurement techniques in the serviceability evaluation of RC structures. The DIC technique was used to accurately measure crack propagation and deflection in RC beams, and the results were compared with those obtained by conventional measurement methods such as linear variable differential transducer (LVDT) and crack width measurement equipment. In addition, 3D models of the specimens were created using 3D scanning and compared with the deformation data measured by DIC to verify their accuracy. The experimental results showed that the DIC and 3D scanning had high accuracy within 4.23% and 2.94%, respectively, compared to the displacement data measured by LVDT, and the proposed algorithm was able to evaluate the correlation between crack width and deflection.
KamW YPampaninSElwoodK. Seismic performance of reinforced concrete buildings in the 22 February Christchurch (Lyttleton) earthquake. Bulletin of the New Zealand Society for Earthquake Engineering, 2011, 44(4): 239–278
[2]
ManapIGalymzhankyzyAOmarovaZUaliyevDTemirbekovDShonC SZhangDKimJ R. Can geopolymer mixture be a solution for utilizing waste glass and basic oxygen furnace slag as aggregates? In: Proceedings of the E3S Web of Conferences. Les Ulis: EDP Sciences, 2025, 04001
[3]
OnopriyenkoZShonC SZhangDKimJ R. Compressive strength and expansion characteristics of BOFS-based geopolymer mortar under different curing regimes. In: Proceedings of the 8th International Conference on Manufacturing, Material and Metallurgical Engineering, ICMMME 2024. Berlin: Springer, 2025, 217–226
[4]
Taffese W Z, Sistonen E. Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions. Automation in Construction, 2017, 77: 1–14
[5]
Gilbert R. The serviceability limit states in reinforced concrete design. Procedia Engineering, 2011, 14: 385–395
[6]
Alexander M, Beushausen H. Durability, service life prediction, and modelling for reinforced concrete structures––Review and critique. Cement and Concrete Research, 2019, 122: 17–29
[7]
Li Y, Zhang J, Guan Z, Chen Y. Experimental study on the correlation between crack width and crack depth of RC beams. Materials, 2021, 14(20): 5950
[8]
Faqih F, Zayed T. Defect-based building condition assessment. Building and Environment, 2021, 191: 107575
[9]
Straub A. Dutch standard for condition assessment of buildings. Structural Survey, 2009, 27(1): 23–35
[10]
Bortolini R, Forcada N. Building inspection system for evaluating the technical performance of existing buildings. Journal of Performance of Constructed Facilities, 2018, 32(5): 04018073
[11]
Zhu Z, German S, Brilakis I. Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation. Automation in Construction, 2011, 20(7): 874–883
[12]
Cho H C, Lee S H, Choi S H, Yi S T, Kang W H, Kim K S. Structural safety inspection of reinforced concrete structures considering failure probabilities of structural members. International Journal of Concrete Structures and Materials, 2023, 17(1): 12
[13]
Park H J, Ryu J R, Woo S H, Choo S Y. An improvement of the building safety inspection survey method using laser scanner and BIM-based reverse engineering. Journal of the Architectural Institute of Korea Planning & Design, 2016, 32(12): 79–90
[14]
Mousa M A, Yussof M M, Hussein T S, Assi L N, Ghahari S. A digital image correlation technique for laboratory structural tests and applications: A systematic literature review. Sensors, 2023, 23(23): 9362
[15]
Poldon J J, Hoult N A, Bentz E C. Distributed sensing in large reinforced concrete shear test. ACI Structural Journal, 2019, 116(5): 235–245
[16]
Tung S H, Weng M C, Shih M H. Measuring the in situ deformation of retaining walls by the digital image correlation method. Engineering Geology, 2013, 166: 116–126
[17]
Yuan Y, Ding C, Wu Z, Zhou J, Zhao Y, Shao W. Compressive failure analysis of seawater-mixed aluminate cement concrete based on digital image correlation technique. Journal of Building Engineering, 2024, 85: 108594
[18]
Xu X, Jin Z, Yu Y, Li N. Damage source and its evolution of ultra-high performance concrete monitoring by digital image correlation and acoustic emission technologies. Journal of Building Engineering, 2023, 65: 105734
[19]
dos Santos J A B, Monte R. Evaluation of the crack bridging ability of waterproofing membranes using indirect tensile test and digital image correlation. Journal of Building Engineering, 2022, 45: 103667
[20]
Jung C, Seo Y, Hong J, Heo J, Cho H C, Ju H. Experimental study on shear strengthening of reinforced concrete beams by fabric-reinforced cementitious matrix. Materials, 2024, 17(17): 4336
[21]
Meiramov D, Ju H, Seo Y, Lee D. Correlation between deflection and crack propagation in reinforced concrete beams. Measurement, 2025, 240: 115527
[22]
SmítkaVŠtronerM. 3D scanner point cloud denoising by near points surface fitting. In: Remondino F, Shortis MR, eds. Proceedings of SPIE 8791, Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection. Bellingham, WA: SPIE, 2013. p. 87910W
[23]
ACI318-19. Building Code Requirements for Structural Concrete (ACI 318-19) and Commentary (ACI 318R-19). Farmington Hills, MI: American Concrete Institute, 2019
[24]
KSF 2405-2022. Test method for compressive strength of concrete. Eumseong: KATS, 2022
[25]
KSF 2423-2021. Test method for splitting tensile strength of concrete. Eumseong: KATS, 2021
[26]
KSD 3504-2021. Steel bars for concrete reinforcement. Eumseong: KATS, 2021
[27]
SPS-FKOCED 0011-7504:2022. Test method for flexural performance of concrete beam (girder) member. Yongin: Korea Construction Engineering Development Collaboratory Management Institute; 2022
[28]
Goszczyńska B, Trąmpczyński W, Tworzewska J. Analysis of crack width development in reinforced concrete beams. Materials, 2021, 14(11): 3043
[29]
Blaber J, Adair B, Antoniou A. Ncorr: Open-source 2D digital image correlation matlab software. Experimental Mechanics, 2015, 55(6): 1105–1122
[30]
Nežerka V, Antoš J, Litoš J, Tesárek P, Zeman J. An integrated experimental-numerical study of the performance of lime-based mortars in masonry piers under eccentric loading. Construction & Building Materials, 2016, 114: 913–924
[31]
Campbell T I, Chouinard K L. Influence of nonprestressed reinforcement on strength of unbonded partially prestressed concrete members. ACI Structural Journal, 1991, 88(5): 546–551
[32]
Janney J R, Hognestad E, McHenry D. Ultimate flexural strength of prestressed and conventionally reinforced concrete beams. Journal of the American Concrete Institute, 1956, 52(6): 601–620
[33]
Kim K S, Lee D H. Nonlinear analysis method for continuous post-tensioned concrete members with unbonded tendons. Engineering Structures, 2012, 40: 487–500
[34]
JongI. Determining deflections of elastic beams: What can the conjugate beam method do that all others cannot? International Journal of Engineering Education, 2010, 26(6): 1422
Fikry A M, Thomas C. Development of a model for the effective moment of inertia of one-way reinforced concrete elements. ACI Structural Journal, 1998, 95(4): 445–455
[37]
UtemuratovaRKarabayAZhangDVarolH A. Shear design optimization of short rectangular reinforced concrete columns using deep learning. In: Proceedings of the International Conference on Civil Engineering and Architecture, Berlin: Springer, 2022