The efficient and large-scale detection of pre-existing disconnections within underground drainage pipe network is crucial for enhancing the service life of these systems and improving the environmental quality of water bodies. Fiber-optic distributed acoustic sensing (DAS) has demonstrated its advantages in large-scale and long-term detection. This study proposes a novel DAS-based method for identifying pre-existing disconnections in high-density polyethylene (HDPE) double-wall corrugated pipes, a common type of drainage pipe, through controlled field experiments. An excitation device was designed to generate steady vibrational excitation at the end of the pipe. Disconnections were localized with meter-level accuracy by analyzing the time–frequency domain characteristics of signals recorded by optical cables deployed within the pipe across different lap regions, thereby validating the feasibility of DAS. Furthermore, the vibration transmission mechanism based on DAS was discussed. These findings contribute to the extensive detection of disconnections in buried HDPE pipes using DAS technology, facilitating the maintenance and upgrading of the underground drainage pipe network.
RygaardMBinningPJAlbrechtsenHJ.Increasing urban water self-sufficiency: new era, new challenges. J Environ Manage2011; 92(1): 185–194.
2.
CampisanoAButlerDWardS, et al. Urban rainwater harvesting systems: research, implementation and future perspectives. Water Res2017; 115: 195–209.
3.
M.o.H.a.U.-R.D.o.t.P.s.R.o. China, Statistical Yearbook of Urban and Rural Construction 2023, 2024.
4.
RajeevPKodikaraJRobertD, et al. Factors contributing to large diameter water pipe failure. Water Asset Manage Int2014; 10(3): 9–14.
5.
LiuZKleinerY.State of the art review of inspection technologies for condition assessment of water pipes. Measurement2013; 46(1): 1–15.
6.
WuYGaoLChaiJ, et al. Overview of health-monitoring technology for long-distance transportation pipeline and progress in DAS technology application. Sensors2024; 24(2): 413.
7.
HarveyRRMcBeanEA.Predicting the structural condition of individual sanitary sewer pipes with random forests. Can J Civ Eng2014; 41(4): 294–303.
8.
HassanSIDangLMMehmoodI, et al. Underground sewer pipe condition assessment based on convolutional neural networks. Autom Constr2019; 106: 102849.
9.
KrizhevskyASutskeverIHintonGE.Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst2012; 25: 1097–1105.
10.
LiDCongAGuoS.Sewer damage detection from imbalanced CCTV inspection data using deep convolutional neural networks with hierarchical classification. Autom Constr2019; 101: 199–208.
11.
HeKZhangXRenS, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016, pp. 770–778.
12.
MeijerDScholtenLClemensF, et al. A defect classification methodology for sewer image sets with convolutional neural networks. Autom Constr2019; 104: 281–298.
13.
RayhanaRJiaoYZajiA, et al. Automated vision systems for condition assessment of sewer and water pipelines. IEEE Trans Autom Sci Eng2020; 18(4): 1861–1878.
14.
WangTLiuZLiaoM, et al. Probabilistic analysis for remaining useful life prediction and reliability assessment. IEEE Trans Reliab2020; 71(3): 1207–1218.
15.
LiuQFanXHeZ.Time-gated digital optical frequency domain reflectometry with 1.6-m spatial resolution over entire 110-km range. Opt Express2015; 23(20): 25988–25995.
16.
HartogAH.An introduction to distributed optical fibre sensors. CRC press, 2017.
17.
WangZLiJFanM, et al. Phase-sensitive optical time-domain reflectometry with Brillouin amplification. Opt Lett2014; 39(15): 4313–4316.
18.
HuangCPengFLiuK.Pipeline inspection gauge positioning system based on optical fiber distributed acoustic sensing. IEEE Sens J2021; 21(22): 25716–25722.
19.
LiuHMaJXuT, et al. Vehicle detection and classification using distributed fiber optic acoustic sensing. IEEE Trans Veh Technol2019; 69(2): 1363–1374.
20.
LiuHMaJYanW, et al. Traffic flow detection using distributed fiber optic acoustic sensing, IEEE Access2018; 6: 68968–68980.
21.
HuangMSalemiMChenY, et al. First field trial of distributed fiber optical sensing and high-speed communication over an operational telecom network. J Light Technol2019; 38(1): 75–81.
22.
LindseyNJDaweTCAjo-FranklinJB.Illuminating seafloor faults and ocean dynamics with dark fiber distributed acoustic sensing. Science2019; 366(6469): 1103–1107.
23.
SladenARivetDAmpueroJP, et al. Distributed sensing of earthquakes and ocean-solid Earth interactions on seafloor telecom cables. Nat Commun2019; 10(1): 5777.
24.
JoussetPCurrentiGSchwarzB, et al. Fibre optic distributed acoustic sensing of volcanic events. Nat Commun2022; 13(1): 1753.
25.
WangY-JZhuoWLiuB, et al. GASF-ConvNeXt-TF algorithm for perimeter security disturbance identification based on distributed optical fiber sensing system. IEEE Internet Things J2024; 11: 17712–17726.
26.
LyuCNiuZTianJ, et al. Identification of intrusion events based on distributed optical fiber sensing in complex environment. IEEE Internet Things J2022; 9(23): 24212–24220.
27.
MuggletonJHuntRRustighiE, et al. Gas pipeline leak noise measurements using optical fibre distributed acoustic sensing. J Nat Gas Sci Eng2020; 78: 103293.
28.
LiYSunKSiZ, et al. Monitoring and identification of wire breaks in prestressed concrete cylinder pipe based on distributed fiber optic acoustic sensing. J Civ Struct Health Monit2024; 14(1): 3–14.
29.
DuanYLiangLTongX, et al. Application of pipeline leakage detection based on distributed optical fiber acoustic sensor system and convolutional neural network. J Phys D Appl Phys2023; 57(10): 105102.
LindseyNJMartinER.Fiber-optic seismology. Ann Rev Earth Planet Sci2021; 49: 309–336.
32.
ChenD.Research of high-performance fiber-optic distributed acoustic sensor based on time-gated digital optical frequency domain reflectometry. PhD Dissertation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 2020.
33.
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.
34.
ChenDLiuQHeZ.Phase-detection distributed fiber-optic vibration sensor without fading-noise based on time-gated digital OFDR. Opt Express2017; 25(7): 8315–8325.
35.
ChenDLiuQHeZ.High-fidelity distributed fiber-optic acoustic sensor with fading noise suppressed and sub-meter spatial resolution. Opt Express2018; 26(13): 16138–16146.
WangYWuHSunY, et al. Underground vibration-print extraction and spatial attenuation law study of ground-borne source sensed by DAS. IEEE Sens J2022; 22(7): 6612–6620.
39.
WuPWuJDongY, et al. Pre-existing concrete pipe disconnection detection based on fiber-optic distributed acoustic sensing. Measurement2025; 254: 117965.
40.
M.o.C.o.t.P.s.R.o. China, Project code for urban and rural sewerage GB 55027-2022, 2022.
41.
ZhanZ.Distributed acoustic sensing turns fiber-optic cables into sensitive seismic antennas. Seismol Res Lett2020; 91(1): 1–15.
42.
MartinR.Noise power spectral density estimation based on optimal smoothing and minimum statistics. IEEE Trans Speech Audio Process2001; 9(5): 504–512.
43.
FangHTanPDuX, et al. Mechanical response of buried HDPE double-wall corrugated pipe under traffic-sewage coupling load. Tunn Undergr Space Technol2021; 108: 103664.
44.
FangHTanPDuX, et al. Numerical and experimental investigation of the effect of traffic load on the mechanical characteristics of HDPE double-wall corrugated pipe. Appl Sci2020; 10(2): 627.
45.
MarstonA.The theory of external loads on closed conduits in the light of the latest experiments. Proc Highw Res Board1930; 9: 138–170.
46.
ChenZZhangC-CShiB, et al. Detecting gas pipeline leaks in sandy soil with fiber-optic distributed acoustic sensing. Tunn Undergr Space Technol2023; 141: 105367.
47.
ChenZZhangC-CShiB, et al. Eavesdropping on wastewater pollution: detecting discharge events from river outfalls via fiber-optic distributed acoustic sensing. Water Res2024; 250: 121069.
