This paper investigates the feasibility and practicability study on the use of Markov chain Monte Carlo (MCMC)-based Bayesian approach for identifying the cement-emulsified asphalt (CA) void of the slab track system utilizing the measured vibration data. A newly developed model class identification algorithm was extended and integrated with the MCMC-based Bayesian approach for the first time to identify the CA mortar void that may partly extend to a neighborhood region. Not only the most probable values of the scaling factors to the mortar stiffness can be calculated, but also the damage probability of model parameters using the posterior probability density function (PDF) can be estimated, and the void can be clearly identified by the MCMC-based Bayesian approach. The proposed methodology was experimentally verified and positive outcomes were obtained. The detection results illustrate that the proposed method not only can successfully assess the void location of the CA mortar but also provide the information of the damage severity, and the posterior PDFs of model parameters can be also calculated by using kernel density estimation to quantitatively describe the uncertainty of the model.
MatiasSRFerreiraPA. Railway slab track systems: review and research potentials. Struct Infrastruct Eng2020; 16(12): 1635–1653.
2.
GautierPE. Slab track: review of existing systems and optimization potentials including very high speed. Constr Build Mater2015; 92: 9–15.
3.
ShengYZQiangFUChengBC, et al. Damage evolution and dynamic response of cement asphalt mortar layer of slab track under vehicle dynamic load. Sci China Technol Sci2014; 57(10): 1883–1894.
4.
ZhuSYCaiCB. Interface damage and its effect on vibrations of slab track under temperature and vehicle dynamic loads. Int J Non Linear Mech2014; 58: 222–232.
5.
YangRSRenJJZhaoPR, et al. Damage analysis and repair technology of the high-speed slab track. Beijing, China: China Railway, 2018.
6.
TianXSZhaoWGDuYL, et al. Detection of mortar defects in ballastless tracks of high-speed railway using transient elastic wave method. J Civ Struct Health Monit2018; 8(1): 151–160.
7.
CheALTangZFengSK. An elastic-wave-based full-wavefield imaging method for investigating defects in a high-speed railway under-track structure. Soil Dyn Earthquake Eng2015; 77: 299–308.
8.
RenJJXiaoLYangRS, et al. Criteria for repairing damages of CA mortar for prefabricated framework-type slab track. Constr Build Mater2016; 110(5): 300–311.
9.
YeXWJinTYunCB. A review on deep learning-based structural health monitoring of civil infrastructures. Smart Struct Syst2019, 24(5): 567–585.
10.
LamHFKatafygiotisLSMickleboroughNC. Application of a statistical model updating approach on Phase I of the IASC-ASCE structural health monitoring benchmark study. Eng Mech2004; 130(1): 34–48.
11.
WangYWNiYQZhangQH, et al. Bayesian approaches for evaluating wind-resistant performance of long-span bridges using structural health monitoring data. Struct Control Health Monit2021; 28: e2699.
12.
YuenKVLamHF. On the complexity of artificial neural networks for smart structures monitoring. Eng Struct2006; 28(7): 977–984.
13.
NgCT. Bayesian model updating approach for experimental identification of damage in beams using guided waves. Struct Health Monit2014; 13(4): 359–373.
14.
TiturusBFriswellMIStarekL. Damage detection using generic elements: Part I. Model updating. Comput Struct2003; 81(24–25): 2273–2286.
15.
TiturusBFriswellMIStarekL. Damage detection using generic elements: Part II. Damage detection. Comput Struct2003; 81(24–25): 2287–2299.
16.
BaruchM. Optimal correction of mass and stiffness matrices using measured modes. AIAA J1982; 20(11): 1623–1626.
17.
ChenJCGarbatJA. Analytical model improvement using modal test results. AIAA J1980; 18(6): 684–690.
18.
HuangYBeckJLLiH. Hierarchical sparse Bayesian learning for structural damage detection: theory, computation and application. Struct Saf2017; 64, 37–53.
19.
ZhuSYCaiCBZhaiWM. Interface damage assessment of railway slab track based on reliability techniques and vehicle-track interactions. J Transp Eng2016; 142(10): 04016041.
20.
BeckJLKatafygiotisLS. Updating models and their uncertainties. I: Bayesian statistical framework. J Eng Mech1998; 124(4): 455–461.
21.
KatafygiotisLSBeckJL. Updating models and their uncertainties. II: model identifiability. J Eng Mech1998; 124(4): 463–467.
22.
LamHFNgCT. A probabilistic method for the detection of obstructed cracks of beam-type structures using spatial wavelet transform. Probab Eng Mech2008; 23(2–3): 237–245.
LamHFHuQWongMT. The Bayesian methodology for the detection of railway ballast damage under a concrete sleeper. Eng Struct2014; 81(12): 289–301.
25.
KuokSCYuenKV. Structural health monitoring of canton tower using Bayesian framework. Smart Struct Syst2012; 10(4–5): 375–391.
26.
KatafygiotisLSLamHF. Tangential-projection algorithm for manifold representation in unidentifiable model updating problems. Earthquake Eng Struct Dyn2002; 31(4): 791–812.
27.
BeckJLAuSK. Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation. J Eng Mech2002; 128(4): 380–391.
28.
ZhouXKimCWZhangFL, et al. Vibration-based Bayesian model updating of an actual steel truss bridge subjected to incremental damage. Eng Struct2022; 260:114226.
29.
ChingJYChenYC. Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging. J Eng Mech2007; 133(7): 816–832.
30.
LamHFYangJHAuSK. Bayesian model updating of a coupled-slab system using field test data utilizing an enhanced Markov chain Monte Carlo simulation algorithm. Eng Struct2015; 102(11): 144–155.
31.
LamHFAlabiSAYangJH. Identification of rail-sleeper-ballast system through time-domain Markov chain Monte Carlo–based Bayesian approach. Eng Struct2017; 140(6): 421–436.
32.
LamHFAdeagboMOYangYB. Time-domain Markov chain Monte Carlo–based Bayesian damage detection of ballasted tracks using nonlinear ballast stiffness model. Struct Health Monit2020; 20(5): 2653–2677.
33.
LamHFYangJHHuQ, et al. Railway ballast damage detection by Markov chain Monte Carlo-based Bayesian method. Struct Health Monit2017; 17(3): 706–724.
34.
HuQShenYJZhuHP, et al. A feasibility study on void detection of cement-emulsified asphalt mortar for slab track system utilizing measured vibration data. Eng Struct2021; 245: 112349.
35.
ZhuSYWangMZZhaiWM, et al. Mechanical property and damage evolution of concrete interface of ballastless track in high-speed railway: experiment and simulation. Constr Build Mater2018; 187(10): 460–473.
36.
FengQSSunKChenHP, et al. Lifetime performance assessment of railway ballastless track systems affected by a mortar interface defect. J Aerosp Eng2019; 32(4): 04019037.
37.
BeckJLYuenKV. Model selection using response measurements: Bayesian probabilistic approach. J Eng Mech2004; 130(2): 192–203.
38.
KatafygiotisLLamHFPapadimitriouC. Treatment of unidentifiability in structural model updating. Adv Struct Eng2000; 3(1): 19–39.
39.
LamHFYangJHAuSK. Markov chain Monte Carlo-based Bayesian method for structural model updating and damage detection. Struct Control Health Monit2018; 25(4): e2140.
40.
HastingsWK. Monte Carlo simulation methods using Markov chain and their applications. Biometrika1970; 57(1): 97–109.
41.
AuSKBeckJL. A new adaptive importance sampling scheme for reliability calculations. Struct Saf1999; 21(2): 135–158.
42.
YuenKV. Bayesian methods for structural dynamics and civil engineering. New York, USA: John Wiley & Sons, 2010.
43.
YangJHLamHF. An efficient adaptive sequential Monte Carlo method for Bayesian model updating and damage detection. Struct Control Health Monit2018; 25(12): e2260.
44.
RenJJWangJLiX, et al. Influence of cement asphalt mortar debonding on the damage distribution and mechanical responses of CRTS I prefabricated slab. Constr Build Mater2020; 230: 116995.
45.
ZhengWYuY. Bayesian probabilistic framework for damage identification of steel truss bridges under joint uncertainties. Adv in Civ Eng2013; 2013: 307171.
