The distributed long-gauge fiber Bragg grating sensing technology has been studied and developed in recent years for structural health monitoring of civil engineering structures. Also, the corresponding damage identification method is one of the research hotspots and still needs to be enhanced. In this article, a novel damage detection method based on the distributed long-gauge fiber Bragg grating sensing technique is proposed to detect and localize damages. The method is based on the advanced complete ensemble empirical mode decomposition adaptive noise algorithm. Measured macrostrain responses from the long-gauge fiber Bragg grating sensors are decomposed into intrinsic mode functions, and the quasi-static macrostrains are extrapolated and extracted. A damage indicator is therefore proposed and built based on the quasi-static macrostrain time history. The effectiveness of the proposed damage detection approach was validated by numerical simulations of a cantilever beam. The robustness of the method was further verified by considering the noise pollution contained within the measured macrostrain. Experiments with a practical cantilever steel beam with different damage scenarios were also conducted and studied. Results proved that the proposed method could not only detect but also locate the damages accurately, and therefore has the promising potential for structural damage detection in civil engineering.
AbdelkaderRKaddourABendiabdellahA, et al. (2018) Rolling bearing fault diagnosis based on an improved denoising method using the complete ensemble empirical mode decomposition and the optimized thresholding operation. IEEE Sensors Journal18(17): 7166–7172.
2.
ArsenaultTJAchuthanAMarzoccaP, et al. (2013) Development of a FBG based distributed strain sensor system for wind turbine structural health monitoring. Smart Materials and Structures22(7): 075027.
3.
BaiLLHanZNLiYF, et al. (2018) A hybrid de-noising algorithm for the gear transmission system based on CEEMDAN-PE-TFPF. Entropy20(5): 361.
4.
BarkeDChiuWK (2005) Structural health monitoring in the railway industry: a review. Structural Health Monitoring4(1): 81–93.
5.
DavenportAG (1961) The Application of statistical concepts to the wind loading of structures. Proceedings of the Institution of Civil Engineers19(4): 449–472.
6.
HibbelerRC (2004) Statics and Mechanics of Materials (SI Edition). Singapore: Prentice Hall.
7.
HongWCaoYWuZS (2016) Strain-based damage-assessment method for bridges under moving vehicular loads using long-gauge strain sensing. Journal of Bridge Engineering21(10): 04016059.
8.
HongWWuZSYangCQ, et al. (2012) Investigation on the damage identification of bridges using distributed long-gauge dynamic macrostrain response under ambient excitation. Journal of Intelligent Material Systems and Structures23(1): 85–103.
9.
HouZKNooriMAmandRS (2000) Wavelet-based approach for structural damage detection. Journal of Engineering Mechanics126(7): 677–683.
10.
HuangNEShenZLongSR, et al. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences454(1971): 903–995.
11.
Kamrujjaman SerkerNHMWuZSLiSZ (2010) A nonphysics-based approach for vibration-based structural health monitoring under changing environmental conditions. Structural Health Monitoring9(2): 145–158.
12.
KimHSMelhemH (2004) Damage detection of structures by wavelet analysis. Engineering Structures26(3): 347–362.
13.
LiSZWuZS (2007a) Development of distributed longgage fiber optic sensing system for structural health monitoring. Structural Health Monitoring6(2): 133–143.
14.
LiSZWuZS (2007b) A non-baseline algorithm for damage locating in flexural structures using dynamic distributed macro-strain responses. Earthquake Engineering and Structural Dynamics36(9): 1109–1125
15.
OuJPLiH (2010) Structural health monitoring in mainland China: review and future trends. Structural Health Monitoring9(3): 219–231.
16.
RenYSuganthanPNSrikanthN (2015) A comparative study of empirical mode decomposition-based short-term wind speed forecasting methods. IEEE Transactions on Sustainable Energy6(1): 236–244.
17.
RippertLWeversMVan HuffelS (2000) Optical and acoustic damage detection in laminated CFRP composite materials. Composites Science and Technology60(14): 2713–2724.
18.
RodriguesCFélixCLageA, et al. (2010) Development of a long-term monitoring system based on FBG sensors applied to concrete bridges. Engineering Structures32(8): 1993–2002.
19.
SanayeiMOnipedeO (1991) Damage assessment of structures using static test data. AIAA Journal29(7): 1174–1179.
20.
SanayeiMSaletnikMJ (1996) Parameter estimation of structures from static strain measurements. II: error sensitivity analysis. Journal of Structural Engineering122(5): 563–572.
21.
SeyedpoorSMYazdanpanahO (2014) An efficient indicator for structural damage localization using the change of strain energy based on static noisy data. Applied Mathematical Modelling38(9–10): 2661–2672.
22.
TorresMEColominasMASchlotthauerG, et al. (2011) A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), Prague, 22–27 May, pp. 4144–4147. New York: IEEE.
23.
WangLLiuZWMiaoQ, et al. (2018) Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings. Mechanical Systems and Signal Processing106: 24–39.
24.
WuBTWuGLuHX, et al. (2017) Stiffness monitoring and damage assessment of bridges under moving vehicular loads using spatially-distributed optical fiber sensors. Smart Materials and Structures26(3): 035058.
25.
WuBTWuGYangCQ, et al. (2016) Damage identification and bearing capacity evaluation of bridges based on distributed long-gauge strain envelope line under moving vehicle loads. Journal of Intelligent Material Systems and Structures27(17): 2344–2358.
26.
WuZHHuangNE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis1(1): 1–41.
27.
XuZDLiuMWuZS, et al. (2011) Energy damage detection strategy based on strain responses for long-span bridge structures. Journal of Bridge Engineering16(5): 644–652.
28.
YeXWNiYQYinJH (2013) Safety monitoring of railway tunnel construction using FBG sensing technology. Advances in Structural Engineering16(8): 1401–1409.
29.
ZhangJGuoSLWuZS, et al. (2015) Structural identification and damage detection through long-gauge strain measurements. Engineering Structures99: 173–183.
30.
ZhangTXiaHGuoWW (2012) Simulation of bridge stochastic wind field using multi-variate Auto-Regressive model. Journal of Central South University of Science and Technology43(3): 1114–1121.
31.
ZhangWYQuZXZhangKQ, et al. (2017) A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting. Energy Conversion and Management136: 439–451.
32.
ZhouZWWanCFWenB, et al. (2019) Structural damage detection with distributed long-gauge FBG sensors under multi-point excitations. Smart Materials and Structures28(9): 095023.
