Advancements in high-resolution distributed fiber optic sensing (DFOS) have demonstrated its effectiveness in quantifying crack widths. However, most existing studies assume that the crack opening direction aligns with the sensor direction. Few studies have addressed the quantification of crack width using distributed fiber optic sensors when the crack opens in other directions. This study aims to develop novel algorithms for quantifying both crack angles and widths using distributed fiber optic sensors through a combination of experimental investigations and numerical simulations. A calibration rig was designed and manufactured to replicate arbitrary-angle crack scenarios, enabling the creation of different crack angles in a two-dimensional plane. Various types of fiber optic cables were employed on the calibration rig to intersect the crack surface at different angles. Numerical simulations were conducted to interpret the strain transfer mechanisms of different fiber optic cables under arbitrary angle crack scenarios. Finally, two algorithms were developed and validated to quantify the crack width and opening direction using DFOS data. Given that the crack opening direction often differs from the fiber optic cable direction in practical applications, this study provides valuable insights for more accurate damage quantification using DFOS and enhances the understanding of optical fiber sensor behavior in real cracking scenarios.
MazniMHusainARShapiaiMI, et al. An investigation into real-time surface crack classification and measurement for structural health monitoring using transfer learning convolutional neural networks and Otsu method. Alexandria Eng J2024; 92: 310–320.
2.
HeZChenWZhangJ, et al. Crack segmentation on steel structures using boundary guidance model. Autom Constr2024; 162: 105354.
3.
ZhangHLiuZMaX, et al. Monitoring of early curing stage of cemented soil using polymer optical fiber sensors and microscopic observation. Constr Build Mater2024; 436: 136888.
4.
ZhengMLeiZZhangK.Intelligent detection of building cracks based on deep learning. Image Vision Comput2020; 103: 103987.
5.
LiRYuJLiF, et al. Automatic bridge crack detection using Unmanned aerial vehicle and faster R-CNN. Constr Build Mater2023; 362: 129659.
6.
HuangHWLiQTZhangDM. Deep learning based image recognition for crack and leakage defects of metro shield tunnel. Tunn Undergr Space Technol2018: 77, 166–176. DOI: https://doi.org/10.1016/j.tust.2018.04.002.
7.
GuoWFengKZhouY, et al. Experimental and numerical investigation on the shear behavior and damage mechanism of segmental joint under compression-shear load. Tunnell Underground Space Technol2023; 139: 105238.
8.
TanXDuJZhangQ, et al. Monitoring restrained shrinkage and cracks of ultra-high-performance concrete (UHPC) using distributed fiber optic sensors. Constr Build Mater2024; 422: 135789.
9.
YanMTanXMahjoubiS, et al. Strain transfer effect on measurements with distributed fiber optic sensors. Autom Constr2022; 139: 104262.
10.
TanXAbu-ObeidahABaoY, et al. Measurement and visualization of strains and cracks in CFRP post-tensioned fiber reinforced concrete beams using distributed fiber optic sensors. Autom Constr2021; 124: 103604.
11.
BarriasACasasJRVillalbaS.A review of distributed optical fiber sensors for civil engineering applications. Sensors2016; 16: 748.
12.
GuoWFengKLuX, et al. Experimental investigation on the damage evolution and failure mechanism of segmental joints based on DOFS and AE. Eng Fail Anal2023; 152: 107471.
13.
ZakiYAAbouhussienAAHassanA, et al. Crack detection and classification of repaired concrete beams by acoustic emission monitoring. Ultrasonics2023; 134: 107068.
14.
LiuHZhangSCoulibalyAA, et al. Monitoring reinforced concrete cracking behavior under uniaxial tension using distributed fiber-optic sensing technology. J Struct Eng2021; 147: 04021212.
15.
SeemabFSchmidtMBaktheerA, et al. Automated detection of propagating cracks in RC beams without shear reinforcement based on DIC-controlled modeling of damage localization. Eng Struct2023; 286: 116118.
16.
ZoubirHRguigMEl AroussiM, et al. Pixel-level concrete bridge crack detection using convolutional neural networks, gabor filters, and attention mechanisms. Eng Struct2024; 314: 118343.
17.
WangJUedaTWangP, et al. Building damage inspection method using UAV-based data acquisition and deep learning-based crack detection. J Civil Struct Health Monit2025; 15(1): 151–171.
18.
BaoXChenL.Recent progress in distributed fiber optic sensors. Sensors2012; 12: 8601–8639.
19.
MaELaiJWangL, et al. Review of cutting-edge sensing technologies for urban underground construction. Measurement2021; 167: 108289.
20.
ZhangLNieJShiB, et al. Calculation the opening of neighboring surface cracks in concrete structure based on OFDR technology. Constr Build Mater2023; 376: 131073.
21.
BadoMFCasasJRKaklauskasG.Distributed sensing (DOFS) in reinforced concrete members for reinforcement strain monitoring, crack detection and bond-slip calculation. Eng Struct2021; 226: 111385.
22.
ZhangSLiuHCoulibalyAAS, et al. Fiber optic sensing of concrete cracking and rebar deformation using several types of cable. Struct Control Health Monit2021; 28: e2664.
23.
MorgeseMYingYTaylorT, et al. Method and theory for conversion of distributed fiber-optic strains to crack opening displacements. J Eng Mech2022; 148: 04022072.
BadoMFCasasJR. A review of recent distributed optical fiber sensors applications for civil engineering structural health monitoring. Sensors2021; 21: 1818.
26.
ZhangSLiuHGovindjeeS, et al. Strain transfer mechanisms and mechanical properties of optical fiber cables. Sensors2022; 22: 9966.
ZhangSLiuHChengJ, et al. A mechanical model to interpret distributed fiber optic strain measurement at displacement discontinuities. Struct Health Monit2021; 20: 2584–2603.
29.
ImaiMFengM.Sensing optical fiber installation study for crack identification using a stimulated Brillouin-based strain sensor. Struct Health Monit2012; 11: 501–509.
30.
BassilAChapeleauXLeducD, et al. Concrete crack monitoring using a novel strain transfer model for distributed fiber optics sensors. Sensors2020; 20: 2220.
31.
ZhaoLTangFLiH-N, et al. Characterization of OFDR distributed optical fiber for crack monitoring considering fiber-coating interfacial slip. Struct Health Monit2023; 22: 180–200.
32.
LinS-QTanD-YYinJ-H, et al. Distributed fiber optic sensing for micro- and macro-crack quantification: an interfacial-fracture-energy-based model. Struct Health Monit2024; 23: 2815–2833.
LiuYBaoY.Intelligent monitoring of spatially-distributed cracks using distributed fiber optic sensors assisted by deep learning. Measurement2023; 220: 113418.
35.
HerbersMRichterBGebauerD, et al. Crack monitoring on concrete structures: comparison of various distributed fiber optic sensors with digital image correlation method. Struct Concrete2023; 24: 6123–6140.
36.
WinklerMMonsbergerCMLienhartW, et al. Assessment of crack patterns along plain concrete tunnel linings using distributed fiber optic sensing. In: Proceedings of the fifth conference on smart monitoring, assessment and rehabilitation of civil structures, Potsdam, Germany, 2019.
37.
GdoutosEE.Fracture mechanics: an introduction. Dordrecht: Springer Netherlands, 2005.
38.
WangDZhuHZhouG, et al. Monitoring shear deformation of sliding zone via fiber Bragg grating and particle image velocimetry. J Rock Mech Geotech Eng2024; 16: 231–241.
39.
LuXZhangS.Mixed-mode crack quantification with distributed fiber optical sensing. In: Proceedings of the 11th European workshop on structural health monitoring (EWSHM 2024). 2024.
40.
LiZGongYChenF.Simulation of mixed mode I-II crack propagation in concrete using toughness-based crack initiation-propagation criterion with modified fracture energy. Theoretical Appl Fract Mech2023; 123: 103701.
41.
LiGZhaoYPangS-S, et al. Effective Young’s modulus estimation of concrete. Cement Concrete Res1999; 29: 1455–1462.
42.
FernandezIBerrocalCGRemplingR.Two-dimensional strain field analysis of reinforced concrete D-regions based on distributed optical fibre sensors. Eng Struct2023; 278: 115562.
43.
ZhangXBroereW.Sensing fiber selection for point displacement measuring with distributed optic fiber sensor. Measurement2022; 197: 111275.
44.
GrunickeUHLienhartWVorwagnerA.Long-term monitoring of visually not inspectable tunnel linings using fibre optic sensing. Geomechanics Tunnelling2021; 14: 19–32.
45.
WangCYangZGaoK, et al. An LSTM-based detection model for breathing cracks with multiple damage positions and degrees of beam-like bridges under vehicle loads. Struct Health Monit2025; Epub ahead of 15 March 2025. doi:10.1177/14759217251319592
46.
RodriguezGCasasJRVillalbaS.Shear crack width assessment in concrete structures by 2D distributed optical fiber. Eng Struct2019; 195: 508–523.
47.
Buda-OżógLZiębaJSieńkowskaK, et al. Distributed fibre optic sensing: reinforcement yielding strains and crack detection in concrete slab during column failure simulation. Measurement2022; 195: 111192.
