The cooperative deformability of sensors embedded into a host material and the measurement accuracy of these sensors affect pavement health monitoring. In consideration of these concerns, this study combined numerical simulation and the response surface method to optimize sensor design and standardize the distributed fiber optic sensors (DFOSs) used in pavement engineering. Numerical models were developed by coupling the discrete element method and the finite difference method. First, on the basis of the discrete-continuous coupled numerical simulation method, two working conditions of unembedded and embedded DFOSs in asphalt mixtures were set up. Virtual specimen models were established, and the numerical simulation method was verified by implementing the indoor four-point bending beam test. Second, the effects of DFOSs on the physical field of asphalt mixtures were analyzed by comparing the two conditions, and optimized evaluation indexes of DFOSs for road use were proposed based on the stress, displacement, and strain difference degrees in consideration of cooperative deformation and measurement accuracy. Last, the Box-Behnken response surface method was used to optimize the cross-section size and elastic modulus of the encapsulation material of the DFOSs, and the optimal design parameter range of the DFOSs was determined. In summary, this study revealed the cooperative deformation law between embedded sensors and asphalt mixtures to facilitate the development and design of DFOSs with high measurement accuracy for pavement applications.
GlisicB. Concise historic overview of strain sensors used in the monitoring of civil structures: the first one hundred years. Sensors2022; 22(6): 2397.
2.
WengXZhuHHChenJ, et al. Experimental investigation of pavement behavior after embankment widening using a fiber optic sensor network. Struct Health Monit2015; 14(1): 46–56.
3.
WangHXiangPJiangL. Strain transfer theory of industrialized optical fiber-based sensors in civil engineering: a review on measurement accuracy, design and calibration. Sens Actuators A Phys2019; 285: 414–426.
4.
LiSWuZ. Development of distributed long-gage fiber optic sensing system for structural health monitoring. Struct Health Monit2007; 6(2): 133–143.
5.
ZhouZHeJChenG, et al. A smart steel strand for the evaluation of prestress loss distribution in post-tensioned concrete structures. J Intell Mater Syst Struct2009; 20(16): 1901–1912.
6.
YouRRenLSongG. A novel OFDR-based distributed optical fiber sensing tape: design, optimization, calibration and application. Smart Mater Struct2020; 29(10): 105017.
7.
CorvagliaPNucciMLanticqV, et al. Realization and testing of hybrid textile reinforced concrete prototype modules sensorized with distributed fiber optic sensors. In: RizzoPMilazzoA (eds) European workshop on structural health monitoring (EWSHM 2022), vol 3. Lecture Notes in Civil Engineering. Singapore: Springer-Verlag Singapore Pte Ltd, 2023, pp. 657–665.
8.
YeHLiuJZhouY, et al. Crack detection and retardation of steel structure by smart CFRP embedding PPP-BOTDA fiber sensors. J Build Eng2022; 49: 104018.
9.
TanXBaoY. Measuring crack width using a distributed fiber optic sensor based on optical frequency domain reflectometry. Measurement2021; 172: 108945.
10.
MengLWangLHouY, et al. A research on low modulus distributed fiber optical sensor for pavement material strain monitoring. Sensors2017; 17(10): 2386.
11.
XiangPWangH. Optical fibre-based sensors for distributed strain monitoring of asphalt pavements. Int J Pavement Eng2018; 19(9): 842–850.
12.
BarriasACasasJRVillalbaS. A review of distributed optical fiber sensors for civil engineering applications. Sensors2016; 16(5): 748.
13.
BadoMFTonelliDPoliF, et al. Digital twin for civil engineering systems: an exploratory review for distributed sensing updating. Sensors2022; 22(9): 3168.
14.
HerSCHuangCY. Effect of coating on the strain transfer of optical fiber sensors. Sensors2011; 11(7): 6926–6941.
15.
AnsariFLiboY. Mechanics of bond and interface shear transfer in optical fiber sensors. J Eng Mech-ASCE1998; 124(4): 385–394.
16.
LiJLZhouZOuJP. Interface transferring mechanism and error modification of embedded FBG strain sensor. In: UddEInaudiD (eds) Smart structures and materials 2004: smart sensor technology and measurement systems. Proceedings of SPIE, Vol. 5384, 2004, pp. 190–198. Bellingham: Spie-Int Soc Optical Engineering.
17.
WuRZhengBFuK, et al. Study on strain transfer of embedded fiber Bragg grating sensors. Opt Eng2014; 53(8): 085105.
18.
HanHMShiBZhangL, et al. Error analysis and experimental research of joint fiber-optic displacement sensor based on shear lag model. Measurement2021; 186: 110106.
19.
TanXBaoYZhangQ, et al. Strain transfer effect in distributed fiber optic sensors under an arbitrary field. Autom Constr2021; 124: 103597.
20.
ZhaoLTangFLiHN, et al. Characterization of OFDR distributed optical fiber for crack monitoring considering fiber-coating interfacial slip. Struct Health Monit2023; 22(1): 180–200.
21.
YanMTanXMahjoubiS, et al. Strain transfer effect on measurements with distributed fiber optic sensors. Autom Constr2022; 139: 104262.
22.
WangHZhouZ. Advances of strain transfer analysis of optical fibre sensors. Pac Sci Rev2014; 16(1): 8–18.
23.
LiuFXuQLiuY. Strain data correction of distributed optical fiber sensors using strain transfer model with variable shear lag parameters. Autom Constr2022; 140: 104311.
24.
ZhengXShiBZhangCC, et al. Strain transfer mechanism in surface-bonded distributed fiber-optic sensors subjected to linear strain gradients: theoretical modeling and experimental validation. Measurement2021; 179: 109510.
25.
WuZZhangHYangC. Development and performance evaluation of non-slippage optical fiber as Brillouin scattering-based distributed sensors. Struct Health Monit2010; 9(5): 413–431.
26.
ZhangQSunYZhangZ, et al. Strain transfer in distributed fiber optic sensor with optical frequency domain reflectometry technology. Opt Eng2019; 58(2): 027109.
27.
WuGZZhangDShanTS, et al. Characterizing the strain transfer on the sensing cable-soil interface based on triaxial testing. Smart Struct Syst2022; 30(1): 63–74.
28.
LiuWWangHZhouZ, et al. Optical fiber-based sensors with flexible encapsulation for pavement behavior monitoring. Struct Control Health Monit2015; 22(2): 301–313.
29.
DongZMaXGongX, et al. Theoretical evaluation of the measurement accuracy of fiber Bragg grating strain sensors within randomly filled asphalt mixtures based on finite element simulation. Struct Control Health Monit2018; 25(1): e2057.
30.
ZhangXQianZZhangM, et al. Numerical simulation for synergetic deformation of optical fiber sensor and asphalt mixture. KSCE J Civ Eng2019; 23(7): 3075–3087.
31.
GuKXiangFLiuC, et al. Insight into the mechanical coupling behavior of loose sediment and embedded fiber-optic cable using discrete element method. Eng Geol2023; 312: 106948.
32.
DongZZhangJMaX, et al. Measurement accuracy analysis of distributed fiber optic sensors for asphalt mixture based on the DEM-FDM coupled method. Struct Control Health Monit2023; 2023: 4093158.
33.
XuZZhangLZhouS. Influence of encasement length and geosynthetic stiffness on the performance of stone column: 3D DEM-FDM coupled numerical investigation. Comput Geotech2021; 132: 103993.
34.
ShiCChenZ. Coupled DEM/FDM to evaluate track transition stiffness under different countermeasures. Constr Build Mater2021; 266: 121167.
35.
CuiXZhouRGuoG, et al. Effects of differential subgrade settlement on slab track deformation based on a DEM-FDM coupled approach. Appl Sci2021; 11(4): 1384.
36.
ChiranjeeviKKumarDHSrinivasaAS, et al. Optimisation of recycled concrete aggregates for cement-treated bases by response surface method. Int J Pavement Eng2023; 24(1): 2179051.
37.
YeLYangMXuL, et al. Optimization of inductive angle sensor using response surface methodology and finite element method. Measurement2014; 48: 252–262.
38.
SchoberPBoerCSchwarteLA. Correlation coefficients: appropriate use and interpretation. Anesth Analg2018; 126(5): 1763–1768.
39.
ShakerHAmeriMAlihaMRM, et al. Evaluating low-temperature fracture toughness of steel slag aggregate-included asphalt mixture using response surface method. Constr Build Mater2023; 370: 130647.
40.
VatanparastMSarkarASahafSA. Optimization of asphalt mixture design using response surface method for stone matrix warm mix asphalt incorporating crumb rubber modified binder. Constr Build Mater2023; 369: 130401.
41.
HeYHeZKimKJ, et al. A Robust interactive desirability function approach for multiple response optimization considering model uncertainty. IEEE Trans Reliab2021; 70(1): 175–187.
42.
GomesFMPereiraFMSilvaAF, et al. Multiple response optimization: analysis of genetic programming for symbolic regression and assessment of desirability functions. Knowl Based Syst2019; 179: 21–33.
43.
DelCastilloEMontgomeryDCMcCarvilleDR. Modified desirability functions for multiple response optimization. J Qual Technol1996; 28(3): 337–345.
