Abstract

This article investigates the time-dependent sensitivity of structural reliability assessment to multisource uncertainties using Lamb wave. To precisely model the influence of local damage on the structure in the course of damage growth, a surface damage effect model is proposed to obtain the equivalent elasticity modulus, which can be coupled with the structure model. The evolution of the surface damage is modeled using the fatigue crack propagation model. Furthermore, by setting up the component and structure failure criteria, the time-dependent reliability model of the structure under multisource uncertainties from Lamb wave detection and material properties is established. The method of score function is employed to evaluate the sensitivity index, which is defined as the derivative of the reliability with respect to the distribution parameters of uncertain variables. A spatial truss structure is used to demonstrate the overall procedure. Numerical results show that the sensitivities indices are time and damage size dependent. The sensitivity contributions from Lamb wave quantification model and the material properties are comparable when the crack size is relatively small. When the crack grows to a larger size, the reliability assessment result is much more sensitive to uncertainties associated with material properties.

References

1.
He
,
J.
,
Huang
,
M.
,
Wang
,
W.
,
Wang
,
S.
, and
Guan
,
X.
,
2021
, “
An Asymptotic Stochastic Response Surface Approach to Reliability Assessment Under Multi-Source Heterogeneous Uncertainties
,”
Reliab. Eng. Syst. Saf.
,
215
, p.
107804
.
2.
Signoret
,
J.-P.
, and
Leroy
,
A.
,
2021
,
Reliability Assessment of Safety and Production Systems: Analysis, Modelling, Calculations and Case Studies
,
Springer Nature
,
Cham, Switzerland
.
3.
Mori
,
N.
,
Biwa
,
S.
, and
Kusaka
,
T.
,
2019
, “
Damage Localization Method for Plates Based on the Time Reversal of the Mode-Converted Lamb Waves
,”
Ultrasonics
,
91
, pp.
19
29
.
4.
Su
,
Z.
,
Zhou
,
C.
,
Hong
,
M.
,
Cheng
,
L.
,
Wang
,
Q.
, and
Qing
,
X.
,
2014
, “
Acousto-Ultrasonics-Based Fatigue Damage Characterization: Linear Versus Nonlinear Signal Features
,”
Mech. Syst. Signal Process
,
45
(
1
), pp.
225
239
.
5.
He
,
J.
,
Leckey
,
C. A.
,
Leser
,
P. E.
, and
Leser
,
W. P.
,
2019
, “
Multi-mode Reverse Time Migration Damage Imaging Using Ultrasonic Guided Waves
,”
Ultrasonics
,
94
, pp.
319
331
.
6.
Catbas
,
F. N.
,
Susoy
,
M.
, and
Frangopol
,
D. M.
,
2008
, “
Structural Health Monitoring and Reliability Estimation: Long Span Truss Bridge Application With Environmental Monitoring Data
,”
Eng. Struct.
,
30
(
9
), pp.
2347
2359
.
7.
He
,
J.
,
Guan
,
X.
,
Peng
,
T.
,
Liu
,
Y.
,
Saxena
,
A.
,
Celaya
,
J.
, and
Goebel
,
K.
,
2013
, “
A Multi-Feature Integration Method for Fatigue Crack Detection and Crack Length Estimation in Riveted lap Joints Using Lamb Waves
,”
Smart Mater. Struct.
,
22
(
10
), pp.
105007
.
8.
Yang
,
J.
,
He
,
J.
,
Guan
,
X.
,
Wang
,
D.
,
Chen
,
H.
,
Zhang
,
W.
, and
Liu
,
Y.
,
2016
, “
A Probabilistic Crack Size Quantification Method Using In-Situ Lamb Wave Test and Bayesian Updating
,”
Mech. Syst. Signal Process
,
78
, pp.
118
133
.
9.
Migot
,
A.
,
Bhuiyan
,
Y.
, and
Giurgiutiu
,
V.
,
2019
, “
Numerical and Experimental Investigation of Damage Severity Estimation Using Lamb Wave–Based Imaging Methods
,”
J. Intell. Mater. Syst. Struct.
,
30
(
4
), pp.
618
635
.
10.
Kucherenko
,
S.
,
2009
, “
Derivative Based Global Sensitivity Measures and Their Link With Global Sensitivity Indices
,”
Math. Comput. Simul.
,
79
(
10
), pp.
3009
3017
.
11.
Lamboni
,
M.
,
Iooss
,
B.
,
Popelin
,
A.-L.
, and
Gamboa
,
F.
,
2013
, “
Derivative-Based Global Sensitivity Measures: General Links With Sobol’ Indices and Numerical Tests
,”
Math. Comput. Simul.
,
87
, pp.
45
54
.
12.
Kucherenko
,
S.
,
Rodriguez-Fernandez
,
M.
,
Pantelides
,
C.
, and
Shah
,
N.
,
2009
, “
Monte Carlo Evaluation of Derivative-Based Global Sensitivity Measures
,”
Reliab. Eng. Syst. Saf.
,
94
(
7
) pp.
1135
1148
.
13.
Saltelli
,
A.
,
Ratto
,
M.
,
Andres
,
T.
,
Campolongo
,
F.
,
Cariboni
,
J.
,
Gatelli
,
D.
,
Saisana
,
M.
, and
Tarantola
,
S.
,
2008
,
Global Sensitivity Analysis: The Primer
,
John Wiley & Sons
,
London, UK
.
14.
Saltelli
,
A.
,
2002
, “
Sensitivity Analysis for Importance Assessment
,”
Risk Anal.
,
22
(
3
), pp.
579
590
.
15.
He
,
J.
, and
Guan
,
X.
,
2017
, “
Uncertainty Sensitivity Analysis for Reliability Problems With Parametric Distributions
,”
IEEE Trans. Reliab.
,
66
(
3
), pp.
712
721
.
16.
Borgonovo
,
E.
, and
Plischke
,
E.
,
2016
, “
Sensitivity Analysis: A Review of Recent Advances
,”
Eur. J. Oper. Res.
,
248
(
3
), pp.
869
887
.
17.
Helton
,
J. C.
,
Johnson
,
J. D.
,
Sallaberry
,
C. J.
, and
Storlie
,
C. B.
,
2006
, “
Survey of Sampling-Based Methods for Uncertainty and Sensitivity Analysis
,”
Reliab. Eng. Syst. Saf.
,
91
(
10–11
), pp.
1175
1209
.
18.
MiarNaeimi
,
F.
,
Azizyan
,
G.
, and
Rashki
,
M.
,
2019
, “
Reliability Sensitivity Analysis Method Based on Subset Simulation Hybrid Techniques
,”
Appl. Math. Model.
,
75
, pp.
607
626
.
19.
Song
,
S.
,
Lu
,
Z.
, and
Qiao
,
H.
,
2009
, “
Subset Simulation for Structural Reliability Sensitivity Analysis
,”
Reliab. Eng. Syst. Saf.
,
94
(
2
), pp.
658
665
.
20.
Talebiyan
,
H.
, and
Mahsuli
,
M.
,
2020
, “
Sampling-Based Reliability Sensitivity Analysis Using Direct Differentiation
,”
ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civil Eng.
,
6
(
2
), p.
04020015
.
21.
Guo
,
J.
, and
Du
,
X.
,
2009
, “
Reliability Sensitivity Analysis With Random and Interval Variables
,”
Int. J. Numer. Methods Eng.
,
78
(
13
), pp.
1585
1617
.
22.
Wei
,
P.
,
Lu
,
Z.
,
Hao
,
W.
,
Feng
,
J.
, and
Wang
,
B.
,
2012
, “
Efficient Sampling Methods for Global Reliability Sensitivity Analysis
,”
Comput. Phys. Commun.
,
183
(
8
), pp.
1728
1743
.
23.
Duhamel
,
R.
,
Robert
,
L.
,
Jia
,
H.
,
Li
,
F.
,
Lardet-Vieudrin
,
F.
,
Manceau
,
J.-F.
, and
Bastien
,
F.
,
2006
, “
Sensitivity of a Lamb Wave Sensor With 2 μm AlN Membrane
,”
Ultrasonics
,
44
, pp.
e893
e897
.
24.
Pilarski
,
A.
,
Rose
,
J. L.
,
Ditri
,
J.
,
Jiao
,
D.
, and
Rajana
,
K.
,
1993
, “Lamb Wave Mode Selection for Increased Sensitivity to Interfacial Weaknesses of Adhesive Bonds,”
Review of Progress in Quantitative Nondestructive Evaluation
,
D. O.
Thompson
, and
D. E.
Chimenti
, eds.,
Springer
,
Boston, MA
, pp.
1579
1585
.
25.
Kundu
,
T.
,
Karpur
,
P.
,
Matikas
,
T. E.
, and
Nicolaou
,
P. D.
,
1996
, “Lamb Wave Mode Sensitivity to Detect Various Material Defects in Multilayered Composite Plates,”
Review of Progress in Quantitative Nondestructive Evaluation
,
D. O.
Thompson
, and
D. E.
Chimenti
, eds.,
Springer
,
Boston, MA
, pp.
231
238
.
26.
Dodson
,
J.
, and
Inman
,
D.
,
2013
, “
Thermal Sensitivity of Lamb Waves for Structural Health Monitoring Applications
,”
Ultrasonics
,
53
(
3
), pp.
677
685
.
27.
Wilcox
,
P. D.
,
Lowe
,
M.
, and
Cawley
,
P.
,
2001
, “
Mode and Transducer Selection for Long Range Lamb Wave Inspection
,”
J. Intell. Mater. Syst. Struct.
,
12
(
8
), pp.
553
565
.
28.
Choudhury
,
S.
,
Thatoi
,
D. N.
,
Maity
,
K.
,
Sau
,
S.
, and
Rao
,
M. D.
,
2018
, “
A Modified Support Vector Regression Approach for Failure Analysis in Beam-Like Structures
,”
J. Fail. Anal. Prev.
,
18
(
4
), pp.
998
1009
.
29.
Tada
,
H.
,
Paris
,
P.
, and
Irwin
,
G.
,
2000
,
The Analysis of Cracks Handbook
,
ASME Press
,
New York
, vol. 2, p.
1
.
30.
Dillström
,
P.
,
Bergman
,
M.
,
Brickstad
,
B.
,
Weilin
,
Z.
,
Sattari-Far
,
I.
,
Andersson
,
P.
,
Sund
,
G.
,
Dahlberg
,
L.
, and
Nilsson
,
F.
,
2008
, “
A Combined Deterministic and Probabilistic Procedure for Safety Assessment of Components With Cracks-Handbook
,”
Swedish Radiation Safety Authority
.
31.
Couroneau
,
N.
, and
Royer
,
J.
,
1998
, “
Simplified Model for the Fatigue Growth Analysis of Surface Cracks in Round Bars Under Mode I
,”
Int. J. Fatigue
,
20
(
10
), pp.
711
718
.
32.
Cox
,
D. R.
, and
Hinkley
,
D. V.
,
1979
,
Theoretical Statistics
,
CRC Press
,
Boca Raton, FL
.
33.
Sprott
,
D. A.
,
2008
,
Statistical Inference in Science
,
Springer Science & Business Media
,
Canada
.
34.
Kadry
,
S.
,
Chateauneuf
,
A.
, and
El-Tawil
,
K.
,
2007
, “
Probabilistic Transformation Method in Reliability Analysis
,”
Turk. J. Eng. Environ. Sci.
,
31
(
3
), pp.
135
148
.
35.
Tedesco
,
J.
,
McDougal
,
W. G.
, and
Ross
,
C. A.
,
2000
,
Structural Dynamics
,
Pearson Education
,
London, UK
.
36.
Newman
,
J.
,
1984
, “
A Crack Opening Stress Equation for Fatigue Crack Growth
,”
Int. J. Fract.
,
24
(
4
), pp.
R131
R135
.
37.
ASTM
,
2017
, “
E1049 - 85(2017) Standard Practices for Cycle Counting in Fatigue Analysis
,”
ASTM
,
West Conshohocken, PA
.
38.
Wang
,
D.
,
He
,
J.
,
Guan
,
X.
,
Yang
,
J.
, and
Zhang
,
W.
,
2018
, “
A Model Assessment Method for Predicting Structural Fatigue Life Using Lamb Waves
,”
Ultrasonics
,
84
, pp.
319
328
.
39.
Berens
,
A. P.
,
1989
, “
NDE Reliability Data Analysis
,” ASM Handbook, Vol.
17
, pp.
689
701
.
40.
Gao
,
C.
,
Fang
,
Z.
,
Lin
,
J.
,
Guan
,
X.
, and
He
,
J.
,
2022
, “
Model Averaging and Probability of Detection Estimation Under Hierarchical Uncertainties for Lamb Wave Detection
,”
Mech. Syst. Signal Process
,
165
, p.
108302
.
You do not currently have access to this content.