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Review Article

Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review

Hatem Mostafa Elwalwal, Shahruddin Bin Hj. Mahzan and Ahmed N. Abdalla
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The Structural Health Monitoring (SHM) serves as an efficient and cost effective way to assist the guided wave study and the development of diagnostic algorithms before conducting time consuming experiments. In this paper introduce a review of Guided waves used for Structural Health Monitoring (SHM). Significant work has been done in guided wave modelling, guided wave generation and sensing and crack detection. In addition, presents the state of the art in these research areas with particular emphasis on guided waves in complex structures literature about guided waves and their applications in assessing the integrity of structures such as pipelines presented. Finally, the state of the art of Lamb-wave-based SHM technologies applied in pipeline structures, for the identification of crack and fatigue crack in science and industry.

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Hatem Mostafa Elwalwal, Shahruddin Bin Hj. Mahzan and Ahmed N. Abdalla, 2017. Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review. Journal of Applied Sciences, 17: 415-428.

DOI: 10.3923/jas.2017.415.428

Received: April 18, 2017; Accepted: June 17, 2017; Published: July 15, 2017

Copyright: © 2017. This is an open access article distributed under the terms of the creative commons attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.


Increasing resources have been put into the development of various approaches for SHM applications. Some of these methods include fiber-optic sensing systems, the statistical pattern recognition methods, the vibration based approaches, the electromechanical impedance based methods and elastic wave based methods. For instance, the application of fiber-optic sensors, particularly Fiber Bragg Grating (FBG) sensors has rapidly accelerated in SHM in recent years1,2. By embedding fiber-optic sensors in structures, it is possible to acquire real-time data on structural variations such as stress or strain. Monitoring data can be utilized to identify deviations from a structure’s original design performance to optimize its operation, repair and maintenance over time3. The SHM problems can also be cast in the context of a statistical pattern recognition paradigm4. Effective feature extraction is first performed by means of multivariate analyses and dimensionality reduction techniques. Here, feature extraction is a step of mining features, which are sensitive to crack of interest, from measured raw signals. Training data are then needed to build a statistical model in the inference stage and the model is used for subsequent decision making for classification and regression problems. Based on the data from uncrack or/and crack systems, supervised or unsupervised learning techniques can be used to identify crack. Data normalization is finally conducted to separate signal changes caused by operational and environmental variations of the system from those due to structural crack. The typical vibration based methods exploit the fact that a change in a structure influences the vibration signature such as the natural frequency of the structure5,6. Natural frequency variations can be used to identify structural vibrations, changes in structural stiffness and cracks. The natural frequency observation methods require a high level of crack and may not be effective in detecting deterioration over time or more subtle failure identification.

One of the most promising methods for active SHM is the integration of smart materials into the structures and utilization of these smart materials as sensors and actuators. Piezoelectric materials are representative among such type of smart materials. Through piezoelectricity and converse piezoelectricity, piezoelectric materials can act as both mechanical sensors and actuators to receive and generate signals7-9. Since the electrical impedance of piezoelectric sensors/actuators intimately bonded onto the structure is directly related to the structure's mechanical impedance, the variation of the electromechanical impedance is monitored over a large frequency spectrum in the high kHz frequency band10. In addition, elastic waves are a type of elastic perturbation that can propagate in a solid and reveal certain characteristics about the propagation medium11.

This study focuses on review of using guided wave based methods for SHM applications. Moreover, more detail of guided wave propagations in crack inspection and wave interactions with structural discontinuities can be easily simulated. Finally, brief description of the important guided wave features such as dispersions and wave mode shapes can be acquired.


The emerging concept of Structural Health Monitoring (SHM) represents one of the enabling technologies that will overcome the aforementioned limitations12-14. The SHM often refers to the process of achieving the crack detection and characterization strategy for engineering structures. The idea of SHM is simply to make manufactured structures more like the human body and build a "Sensing skin" for those structures, which is practically implemented by permanently attaching an onboard network of actuators/sensors on the monitored structures15. The SHM process involves the observation of the system over time utilizing periodically sampled dynamic responses from the sensory network, the extraction of differential features caused by crack from these measurements and the comprehensive analysis of these features to determine the current state of structural health. The SHM can be either passive or active. Passive SHM infers the state of the structure by utilizing passive sensors, so this scheme only "Listens" to the structure without interacting with it. It has been shown that the reliability of SHM systems increases when the sensors do not just "Listen" but function as both actuators and sensors16. Active SHM utilizes active actuators/sensors that integrate the structure to identify the appearance of crack and its associated severity.

A complete SHM methodology should be able to (1) Identify crack occurrence in the structure, if any, (2) Locate structural crack and (3) Quantitatively describe the severity of crack. A more detailed general discussion of SHM can be found by Worden and Dulieu-barton17.

The process of SHM is organized by the four steps as shown in Fig. 118. All of researches in the field of SHM address some parts of the process. Operational Evaluation addresses life-safety and/or economic issues, definition of possible crack, environmental and/or operational conditions and data management constraints. The step of data acquisition, fusion and cleansing discusses how to select excitation and sensing methods and to configure data collection parameters such as strain, displacement and acceleration. In addition, for better feature extraction performance, the data cleansing process is performed for noise removal, spike removal and outlier removal.

Table 1:Crack identification levels
Image for - Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review

Image for - Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review
Fig. 1: Structural Health Monitoring (SHM) process

The step of feature extraction and information condensation addresses data analysis parameters and signal processing methods like time and/or frequency analysis. The last step, statistical model development for feature discrimination, discusses how to determine changes between uncrack and crack structures and how to develop a model based on only uncrack structures.

This process is generally classified into two types, supervised learning and unsupervised learning mode. The supervised learning mode provides the information about crack presence and its possible location. The unsupervised learning mode is used for crack type discrimination, the extent of crack and the remaining lifetime of structures. For crack identification, SHM technology requires including all the crack information obtained from both supervised and unsupervised learning modes.

Crack identification level for SHM technology was first proposed by Rytter19, separated into four steps. Farrar and Worden18 divided the crack identification steps into five levels as shown in Table 1. Therefore the importance of the crack classification when multiple crack mechanisms are active, the type and the extent of the crack were organized into the separate steps for crack identification.

Each crack level requires all of the lower-level information. Levels 1 through 4 are associated with crack diagnostic process. On the other hand, level 5 is distinguished from others because this step is to develop validated simulation models to expect structural failure based on the understanding of the physics of failure. Hence, the remaining lifetime of structures/components can be predicted by the model development. For this study, the crack diagnostic process (Level 1~4) is focused on experimental investigation for the SHM technology.


It is well established that structural health is directly related to structural performance that can be regarded as the primary factor in establishing the operational safety of the structure. There are different kinds of methods that can be used to detect the health of a structure and some of these are conventional methods such as vibration-based or parameter estimation methods, while others are based on new concepts. Many of the current methods for crack detection use the system or subspace identification technique; others use crack detection algorithms that bypass system identification and rely directly on the measured data to identify the crack. The vibration monitoring of a structure has gained popularity over the past decade due to the relative ease of instrumentation involved and the powerful system identification techniques developed for structural health monitoring. The main idea here is to replace the visual, systematic inspections by health monitoring systems that can continuously acquire and analyze vibration data and allow identification of crack at an early stage. More recently, a number of researchers have used neural networks for crack detection that can be considered to be a two-phase method involving a pattern generation/training phase and a pattern recognition phase. Another concept used for crack detection, known as the Precursor Transformation Method (PTM) is based on determining the causes (precursors) of change in the measured state of the structure under non-variable loading conditions (e.g., dead loads in bridges). Based on different methods and environments involved, a variety of parameters and criteria are used for measurement and crack detection. The different types of structural health monitoring, signal processing and analysis methods used (proposed) in the literature as indicated in Fig. 2.

System and subspace identification: The system identification technique is one that constructs a model of the cracked structure. By comparing the model of the cracked structure with that of the uncracked structure, the crack is detected. Lindner and Goff20 did some research in this field. Liu and Rao21 used a parameter identification method to determine the crack of a structure. With the subspace system identification algorithm, a structure state-space model was obtained.

Image for - Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review
Fig. 2: Structural health monitoring

The identified state-space model was then transformed into two special realization forms for determination of the equations of motion of the multiple degree-freedom structure. The parameters of the equations of motion, mass and stiffness matrices or crack indices were used to determine the location and extent of the crack. This method can also be extended for the health monitoring of substructural systems. Mevel et al.22 used Hereto, a statistical local approach based on covariance-driven stochastic subspace identification, to detect the structural crack. The approach was applied to vibration data measured and ambient response data were measured right before and after applying a crack pattern. With the applications to a sports car and the vibration data measured on the bridge Z24 in Switzerland, it illustrated that the method allows the early detection of a vibration-induced fatigue problem. Mevel et al.22,23 also did some research in a detection algorithm design with the statistical local approach based on stochastic subspace-based identification. This approach dealt with the early detection of slight deviations in usual working conditions. With output-only samples measured on a few industrially relevant examples, such as a steel subframe structure, the approach was proved to be efficient and capable of detecting slight changes in their eigenstructures. Tasker et al.24 did some research on structural crack detection using subspace estimation. With an on-line subspace modal parameter estimation algorithm for crack detection, it showed that the method was able to detect small changes in structural properties through an innovation vector. Experimental evaluation demonstrated that the method could rapidly detect the occurrence of a change in the dynamic system using multiple sensors. The time-varying behaviour was captured in real-time via a graphical display of the norm of the innovation vector of the system.


For guided wave excitation and sensing, various transducers have been used, such as Piezoelectric Wafer Active Sensors (PWAS) (Fig. 3a), comb transducer (Fig. 3b), Macro Fiber Composites (MFC) (Fig. 3c), wedge transducers (Fig. 3d), fiber optics (Fig. 3e), electromagnetic acoustic transducers (EMAT)25,26 (Fig. 3f), air-coupled transducers (Fig. 3g) and laser devices (Fig. 3h)27,28. Among these transducers, the low profile PZT are widely used for guided wave excitation and sensing. The PZT are small and light and suitable for integration into host structures (surface-mounting or embedding in composites) without significant intrusion, serving as good candidates for built-in transducers.

Image for - Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review
Fig. 3(a-h):
Examples of various transducers (a) PWAS29, (b) Comb transducer30, (c) MFC31, (d) Wedge transducers32, (e) FBG33, (f) EMAT transducer27, (g) air-coupled transducer34 and (h) laser transducers35

Moreover, PZT can serve several purposes, such as high-bandwidth strain sensors and exciters, resonators and embedded modal sensors12. Recently, the laser devices, such as the high power pulse laser and the laser Doppler vibrometer, have emerged for non-contact guided wave applications4. The pulse laser can excite high energy wide band guided waves based on either the thermal elastic effect or structure surface oblation.

Image for - Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review
Fig. 4(a-f):
Schematics of PWAS applications (a) Pitch-catch sensing, (b) Pulse-echo sensing, (c) Thickness sensing mode, (d) Impact/AE detection, (e) PWAS phased array and (f) E/M impedance method

The laser Doppler vibrometer can measure the velocity or displacement at the sensing point on structural surface in the direction of the laser beam based on the Doppler effect. Their non-contact and remote sensing natures have attracted a lot of attention.

With the embedded guided wave excitation and sensing abilities, PWAS have be used for various SHM applications8,29,36, such as (1) Active sensing of far-field crack using pitch-catch (Fig. 4a), pulse-echo (Fig. 4b) and phased array (Fig. 4e) methods, (2) Active sensing of near field crack using E/M impedance method (Fig. 4f) and thickness sensing mode (Fig. 4c) and (3) Passive sensing of Acoustic Emission (AE) events (Fig. 4d).


The crack or faults such as cracking, delamination, unbonding or the loosening of fasteners will change the physical properties of a structure37. A change in any one of the physical properties of stiffness, damping or mass will in general alter the behavior of the structure. Over the past 20 years, the idea of using physical properties and responses to assess the integrity of structures and machine elements has captured the attention of many researchers. Research in this area has been conducted on many fronts in a number of different disciplines are presented in Table 2.


Many researchers have used piezoelectric sheet elements as sensors in active controllable systems57-60 and in structural health monitoring systems61-63, since such piezoelectric sensors have advantages such as compactness, sensitivity over a large strain bandwidth in the monitored structure. It was demonstrated that for high frequency cases a dynamical piezoelectric sensor model should be used to consider dynamic sensing effects.

Among the most common sensing systems applied for GW-SHM, such as PZT transducers, PZT-based sensing systems in terms of GWs deliver excellent performance in detection and location of cracks.

The studies have verified that GW wave sensing systems for SHM have many advantages for identifying crack in structures, beam, plate and pipeline summarized in Table 3.

Table 2: Survey of guided wave crack detection technique
Image for - Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review
Image for - Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review

Table 3: Guided wave for structural health monitoring
Image for - Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review
Image for - Crack Inspection Using Guided Waves (GWs)/Structural Health Monitoring (SHM): Review


In the pipeline industry, a single method for detecting or monitoring damage in a pipeline does not currently exist. Instead, industries typically implement a combination of several different techniques. For the oil and natural gas pipeline industry, destructive and non-destructive inspection techniques are commonly used together to ensure the integrity of transmission lines78-82. These techniques typically require the pipeline system to be temporarily taken out of operation. The most common destructive technique is a hydrostatic test. For oil pipelines, a hydrostatic test involves pressurizing the pipeline to a point greater than the maximum operating pressure. The pressure is then observed for several hours to determine if any leaks are present, because a hydrostatic test could potentially cause a leak or rupture, all the hazardous materials in the pipeline must be replaced with water to prevent environmental damage, because of service interruptions and water removal difficulties, hydrostatic testing is not used with natural gas pipelines.

When the geometry of the pipeline permits, non-destructive techniques are primarily used to ensure the structure’s integrity. Such techniques commonly involve sending a magnetic flux or ultrasonic inspection device down the inside of the pipeline. The size of the device available limits the smallest size pipe that can be tested and the radius of bends also limits the ability to use a particular device. These devices perform best in oil pipelines because petroleum products act as a good coupling between the instrument and the pipe wall. Accordingly, these techniques do not require oil pipelines to be emptied, contrary to hydrostatic testing. However, natural gas pipelines are more complicated because a gas does not provide good coupling for the testing device. Therefore, operators of natural gas pipelines have turned to direct assessment procedures for the determination of the integrity of their systems.

As evidenced by the documented cases of pipeline accidents, the current approaches used in industry to monitor the structural integrity of pipelines is not 100% effective. Even though pipelines are one of the safest modes of energy transportation, there is still justification for seeking improvements. The associated costs of property damage from accidents are quite significant, not to mention the enormous loss from each and every fatality. Also, the implementation of both destructive and non-destructive inspection techniques requires the pipeline to be taken temporarily out of service, which adds to the costs to an operator. Therefore, the development of a more reliable, cheaper monitoring system would have countless advantages for pipeline operators.

Hence, innovative monitoring systems and defects diagnosis techniques should be in place to insure the sustainability of the infrastructure, i.e. pipelines and the associated equipment to insure their integrity and continuity80,83.


This study focused on various vibration-based SHM methods, elastic-wave-based method, referred as guided wave method which employed as the fundamental tool for crack inspection. The GW method is an active SHM technology, which is a combination of Ultrasonic testing and Acoustic emission approaches. The technique is a global SHM method, which also has capability to detect local cracks of any structure. A complete SHM methodology should be able to identify crack occurrence in the structure, if any, locate structural crack and quantitatively describe the severity of crack. In addition, GW method has a number of advantages such as simple inspection methodology; time- and cost effectiveness; ability of wide area inspection with a limited number of transducers, fast and repeatable inspection capability; sensitivity to small cracks; and mode and frequency tuning capability.


This study presents a state-of-the-art review on various methodologies for the integration of GWs and SHM. A broad classification of important research works was presented along with their shortcomings and contributions. A comparative analysis in tabular form of various guided waves approaches has also been given. This study is very helpful for those who want to build up a level of understanding in the area of pipeline crack detection and classification based on different techniques including artificial neural networks.


Authors very thankful to Faculty of Mechanical and Manufacturing. Eng., University Tun Hussein Onn Malaysia (UTH) for financial support (RDU1503116) this project.

1:  Majumder, M., T.K. Gangopadhyay, A.K. Chakraborty, K. Dasgupta and D.K. Bhattacharya, 2008. Fibre Bragg gratings in structural health monitoring-Present status and applications. Sensors Actuators A: Phys., 147: 150-164.
CrossRef  |  Direct Link  |  

2:  2011. Application of fibre Bragg grating sensors for structural health monitoring of an adaptive wing. Smart Mater. Struct., Vol. 20.

3:  Glisic, B. and D. Inaudi, 2008. Fibre Optic Methods for Structural Health Monitoring. John Wiley and Sons, New York, ISBN: 9780470517802, Pages: 276.

4:  Sohn, H., 2014. Noncontact laser sensing technology for structural health monitoring and nondestructive testing. Proceedings of the Sensors and Smart Structures Technologies for Civil, Mechanical and Aerospace Systems, May 6, 2014, San Diego, CA., USA., pp: 10-12.

5:  Pandey, A.K., M. Biswas and M.M. Samman, 1991. Damage detection from changes in curvature mode shapes. J. Sound Vibr., 145: 321-332.
CrossRef  |  Direct Link  |  

6:  Worden, K. and D.J. Inman, 2010. Modal Vibration Methods in Structural Health Monitoring. In: Encyclopedia of Aerospace Engineering, Blockley, R. and W. Shyy (Eds.). Vol. 9, Wiley, New York.

7:  Qing, X.P., S.J. Beard, A. Kumar, T.K. Ooi and F.K. Chang, 2007. Built-in sensor network for structural health monitoring of composite structure. J. Intell. Mater. Syst. Struct., 18: 39-49.
CrossRef  |  Direct Link  |  

8:  Giurgiutiu, V., B. Xu and W. Liu, 2010. Development and testing of high-temperature piezoelectric wafer active sensors for extreme environments. Struct. Health Monit., 9: 513-525.
CrossRef  |  Direct Link  |  

9:  Doherty, C. and W.K. Chiu, 2012. Scattering of ultrasonic-guided waves for health monitoring of fuel weep holes. Struct. Health Monit., 11: 27-42.
CrossRef  |  Direct Link  |  

10:  Zagrai, A., D. Doyle, V. Gigineishvili, J. Brown, H. Gardenier and B. Arritt, 2010. Piezoelectric wafer active sensor structural health monitoring of space structures. J. Intell. Mater. Syst. Struct., 21: 921-940.
CrossRef  |  Direct Link  |  

11:  Achenbach, J., 2012. Wave Propagation in Elastic Solids. Elsevier, USA., ISBN: 9780080934716, Pages: 440.

12:  Giurgiutiu, V. and A. Cuc, 2005. Embedded non-destructive evaluation for structural health monitoring, damage detection and failure prevention. Shock Vibr. Digest, 37: 83-105.
Direct Link  |  

13:  Kundu, T., S. Das and K.V. Jata, 2009. Health monitoring of a thermal protection system using Lamb waves. Struct. Health Monit., 8: 29-45.
CrossRef  |  Direct Link  |  

14:  Mahzan, S. and M.M.F. Elghanudi, 2006. Feasibility study of structural health monitoring towards pipeline corrosion monitoring: A review. ARPN J. Eng. Applied Sci., 11: 8673-8678.
Direct Link  |  

15:  Chang, F.K., J.F. Markmiller, J.B. Ihn and K.Y. Cheng, 2007. A potential link from damage diagnostics to health prognostics of composites through built-in sensors. J. Vibr. Acoust., 129: 718-729.
CrossRef  |  Direct Link  |  

16:  Boller, C., C. Biemans, W.J. Staszewski, K. Worden and G.R. Tomlinson, 1999. Structural damage monitoring based on an actuator-sensor system. Proceedings of the SPIE's 6th International Symposium on Smart Structures and Materials, March 1-4, 1999, Newport Beach, CA., USA -.

17:  Worden, K. and J.M. Dulieu-Barton, 2004. An overview of intelligent fault detection in systems and structures. Struct. Health Monit., 3: 85-98.
CrossRef  |  Direct Link  |  

18:  Farrar, C.R. and K. Worden, 2007. An introduction to structural health monitoring. Philos. Trans. Royal Soc. Lon. A: Math. Phys. Eng. Sci., 365: 303-315.
CrossRef  |  Direct Link  |  

19:  Rytter, A., 1993. Vibrational based inspection of civil engineering structures. Ph.D. Thesis, University of Aalborg, Aalborg.

20:  Lindner, D.K. and R. Goff, 1993. Damage detection, location and estimation for space trusses. Proceedings of the SPIE's Symposium on Smart Structures and Materials, February 1-4, 1993, Albuquerque, New Mexico -.

21:  Liu, P. and V.S. Rao, 2000. Structural health monitoring using parameter identification methods. Proceedings of the SPIE's 7th Annual International Symposium on Smart Structures and Materials, March 6-8, 2000, Newport Beach, CA., USA., pp: 792-805.

22:  Mevel, L., M. Basseville, A. Benveniste, M. Goursat, M. Abdelghani and L. Hermans, 1999. On the application of a subspace-based fault detection method. Proceedings of the 17th International Modal Analysis Conference, February 1999, Kissimmee, FL., pp: 35-41.

23:  Mevel, L., L. Hermans and H. van der Auweraer, 2000. Health monitoring of a concrete three-span bridge. Proceedings of the IMAC-XVIII: A Conference on Structural Dynamics, February 7-10, 2000, San Antonio, TX., USA., pp: 690-694.

24:  Tasker, F., B. Dunn and S. Fisher, 1999. Online structural damage detection using subspace estimation. Proceedings of the Aerospace Conference, Volume 2, March 7, 1999, Snowmass at Aspen, CO., USA., pp: 173-179.

25:  Bhuiyan, M.Y., Y. Shen and V. Giurgiutiu, 2016. Guided wave based crack detection in the rivet hole using global analytical with local fem approach. Materials, 9: 602-619.
Direct Link  |  

26:  2016. Linear and nonlinear guided wave imaging of impact damage in CFRP using a probabilistic approach. Materials, Vol. 9.
CrossRef  |  

27:  Wilcox, P., M. Lowe and P. Cawley, 2005. Omnidirectional guided wave inspection of large metallic plate structures using an EMAT array. IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 52: 653-665.
CrossRef  |  Direct Link  |  

28:  Wang, S., S. Huang, W. Zhao and Z. Wei, 2015. 3D modeling of circumferential SH guided waves in pipeline for axial cracking detection in ILI tools. Ultrasonics, 56: 325-331.
CrossRef  |  Direct Link  |  

29:  Santoni, G.B., L. Yu, B. Xu and V. Giurgiutiu, 2007. Lamb wave-mode tuning of piezoelectric wafer active sensors for structural health monitoring. J. Vibr. Acoust., 129: 752-762.
CrossRef  |  Direct Link  |  

30:  Rose, J.L., 2000. Guided wave nuances for ultrasonic nondestructive evaluation. IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 47: 575-583.
CrossRef  |  Direct Link  |  

31:  Raghavan, A.C. and C.E.S. Cesnik, 2007. Review of guided-wave structural health monitoring. Shock Vibr. Digest, 39: 91-114.
Direct Link  |  

32:  Culshaw, B., S.G. Pierce and W.J. Staszekski, 1998. Condition monitoring in composite materials: An integrated systems approach. Proc. Inst. Mech. Eng. Part I: J. Syst. Control Eng., 212: 189-202.
CrossRef  |  Direct Link  |  

33:  2009. Acoustic-wave-mode separation using a distributed Bragg grating sensor. Smart Mater. Struct., Vol. 18.

34:  Ke, W., M. Castaings and C. Bacon, 2009. 3D finite element simulations of an air-coupled ultrasonic NDT system. NDT & E Int., 42: 524-533.
CrossRef  |  Direct Link  |  

35:  2014. Monitoring of pipelines in nuclear power plants by measuring laser-based mechanical impedance. Smart Mater. Struct., Vol. 23.

36:  Jing, Y., Z. Hongping and H. Minshui, 2010. Numerical study of structure health monitoring using surface-bonded and embedded PZT transducers. Proceedings of the International Conference on Mechanic Automation and Control Engineering, June 26-28, 2010, Wuhan, China -.

37:  Zaleha, M., S. Mahzan and I.M. Izwana, 2014. Damage size classification of natural fibre reinforced composites using neural network. Adv. Mater. Res., 911: 60-64.
CrossRef  |  Direct Link  |  

38:  Lowe, M.J.S., D.N. Alleyne and P. Cawley, 1998. Defect detection in pipes using guided waves. Ultrasonics, 36: 147-154.
CrossRef  |  Direct Link  |  

39:  Liew, C.K. and M. Veidt, 2009. Pattern recognition of guided waves for damage evaluation in bars. Pattern Recognit. Lett., 30: 321-330.
CrossRef  |  Direct Link  |  

40:  Lu, B.T., 2014. Further study on crack growth model of buried pipelines exposed to concentrated carbonate-bicarbonate solution. Eng. Fract. Mech., 131: 296-314.
CrossRef  |  Direct Link  |  

41:  Tan, J.P., S.T. Tu, G.Z. Wang and F.Z. Xuan, 2015. Characterization and correlation of 3-D creep constraint between axially cracked pipelines and test specimens. Eng. Fract. Mech., 136: 96-114.
CrossRef  |  Direct Link  |  

42:  Pan, J.H., Z.C. Fan and N.S. Zong, 2016. Research on weld cracking of TP321H stainless steel pipeline under elevated temperature. Int. J. Pressure Vessels Piping, 148: 1-8.
CrossRef  |  Direct Link  |  

43:  Cheng, A. and N.Z. Chen, 2017. Fatigue crack growth modelling for pipeline carbon steels under gaseous hydrogen conditions. Int. J. Fatigue, 96: 152-161.
CrossRef  |  Direct Link  |  

44:  Liu, B., L.Y. He, H. Zhang, Y. Cao and H. Fernandes, 2017. The axial crack testing model for long distance oil-gas pipeline based on magnetic flux leakage internal inspection method. Measurement, 103: 275-282.
CrossRef  |  Direct Link  |  

45:  Eybpoosh, M., M. Berges and H.Y. Noh, 2017. An energy-based sparse representation of ultrasonic guided-waves for online damage detection of pipelines under varying environmental and operational conditions. Mech. Syst. Signal Process., 82: 260-278.
CrossRef  |  Direct Link  |  

46:  Xu, J., Z. Xu and X. Wu, 2012. Research on the lift-off effect of generating longitudinal guided waves in pipes based on magnetostrictive effect. Sensors Actuators A: Phys., 184: 28-33.
CrossRef  |  Direct Link  |  

47:  Wang, X., P.W. Tse, C.K. Mechefske and M. Hua, 2010. Experimental investigation of reflection in guided wave-based inspection for the characterization of pipeline defects. NDT E Int., 43: 365-374.
CrossRef  |  Direct Link  |  

48:  Cobb, A.C., H. Kwun, L. Caseres and G. Janega, 2012. Torsional guided wave attenuation in piping from coating, temperature and large-area corrosion. NDT E Int., 47: 163-170.
CrossRef  |  Direct Link  |  

49:  Peter, W.T. and X. Wang, 2013. Characterization of pipeline defect in guided-waves based inspection through matching pursuit with the optimized dictionary. NDT & E Int., 54: 171-182.
CrossRef  |  Direct Link  |  

50:  Clough, M., M. Fleming and S. Dixon, 2017. Circumferential guided wave EMAT system for pipeline screening using shear horizontal ultrasound. NDT E Int., 86: 20-27.
CrossRef  |  Direct Link  |  

51:  Lowe, P.S., R. Sanderson, S.K. Pedram, N.V. Boulgouris and P. Mudge, 2015. Inspection of pipelines using the first longitudinal guided wave mode. Phys. Procedia, 70: 338-342.
CrossRef  |  Direct Link  |  

52:  Jiang, Y. and M. Chen, 2012. Researches on the fatigue crack propagation of pipeline steel. Energy Procedia, 14: 524-528.
CrossRef  |  Direct Link  |  

53:  Koppe, E., M. Bartholmai and J. Prager, 2012. Device concept for the generation of guided waves for early damage detection. Procedia Eng., 47: 1185-1188.
CrossRef  |  Direct Link  |  

54:  Sun, L. and Y. Li, 2010. Acoustic emission sound source localization for crack in the pipeline. Proceedings of the Control and Decision Conference, May 26-28, 2010, China -.

55:  Kim, J., M. Choi and J. Lee, 2011. Employing magnetic sensor array for inspecting cracks in a pipeline. Proceedings of the Sensors Applications Symposium, February 22-24, 2011, San Antonio, TX., USA -.

56:  Piddubniak, O., N. Piddubniak and S. Brzozowska, 2013. Analysis of acoustic echo-signal from gas pipeline with long crack. Proceedings of the 18th International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory, September 23-26, 2013, Lviv, Ukraine, pp: 219-225.

57:  Lee, C.K. and T.C. O'Sullivan, 1991. Piezoelectric strain rate gages. J. Acoust. Soc. Am., 90: 945-953.
CrossRef  |  Direct Link  |  

58:  Qiu, J. and J. Tani, 1995. Vibration control of a cylindrical shell using distributed piezoelectric sensors and actuators. J. Intell. Mater. Syst. Struct., 6: 474-481.
CrossRef  |  Direct Link  |  

59:  1997. Acoustic wave sensors: Design, sensing mechanisms and applications. Smart Mater. Struct., Vol. 6.

60:  1999. Finite element simulation of smart structures using an optimal output feedback controller for vibration and noise control. Smart Mater. Struct., Vol. 8.

61:  Samuel, P.D. and D.J. Pines, 1997. Health monitoring and damage detection of a rotorcraft planetary geartrain system using piezoelectric sensors. Proceedings of the SPIE's 4th Annual Symposium on Smart Structures and Materials, March 3-6, 1997, San Diego, CA., USA -.

62:  1997. Determination of the stress components of an array of piezoelectric sensors: A numerical study. Smart Mater. Struct., Vol. 6.

63:  Wang, B.T. and R.L. Chen, 2000. The use of piezoceramic transducers for smart structural testing. J. Intell. Mater. Syst. Struct., 11: 713-724.
CrossRef  |  Direct Link  |  

64:  An, Y.K. and H. Sohn, 2012. Integrated impedance and guided wave based damage detection. Mech. Syst. Signal Process., 28: 50-62.
CrossRef  |  Direct Link  |  

65:  Rose, J.L., Y. Cho and M.J. Avioli, 2009. Next generation guided wave health monitoring for long range inspection of pipes. J. Loss Prev. Process Ind., 22: 1010-1015.
CrossRef  |  Direct Link  |  

66:  Na, W.S., 2017. Possibility of detecting wall thickness loss using a PZT based structural health monitoring method for metal based pipeline facilities. NDT E Int., 88: 42-50.
CrossRef  |  Direct Link  |  

67:  Baltazar, A., E. Rojas and R. Mijarez, 2015. Structural health monitoring in cylindrical structures using helical guided wave propagation. Phys. Procedia, 70: 686-689.
CrossRef  |  Direct Link  |  

68:  Yuan, L. and W. Yan, 2009. Crack detection in structural systems using electro-mechanical signatures. Proceedings of the Asia-Pacific Power and Energy Engineering Conference, March 27-31, 2009, Wuhan, China, pp: 1-4.

69:  Mahadevan, S., Y. Ling and S. Sankararaman, 2011. Confidence assessment in model-based structural health monitoring. Proceedings of the IEEE Aerospace Conference, March 5-12, 2011, Big Sky, MT., USA -.

70:  Packo, P., L. Ambrozinski and T. Uhl, 2011. Structure damage modelling for guided waves-based SHM systems testing. Proceedings of the 4th International Conference on Modeling, Simulation and Applied Optimization, April 19-21, 2011, Kuala Lumpur, Malaysia, pp: 1-6.

71:  Sun, M., F. Wan and Z. Qin, 2012. Health monitoring for propagating crack faults. Proceedings of the IEEE Conference on Prognostics and System Health Management, May 23-25, 2012, Beijing, China, pp: 1-6.

72:  Gaith, M., M.E.H. Assad, A. Sedaghat, M. Hiyasat and S. Alkhatib, 2015. Neural network usage in structural crack detection. Proceedings of the International Conference on Industrial Engineering and Operations Management, March 3-5, 2015, Dubai, United Arab Emirates, pp: 1-5.

73:  Qatu, K.M., A. Abdelgawad and K. Yelamarthi, 2016. Structure damage localization using a reliable wave damage detection technique. Proceedings of the International Conference on Electrical, Electronics and Optimization Techniques, March 3-5, 2016, Chennai, India, pp: 1959-1962.

74:  He, J., Y. Ran, J. Yang and W. Zhang, 2016. A novel crack size quantification method based on lamb wave simulation. Proceedings of the Prognostics and System Health Management Conference, October 19-21, 2016, Chengdu, Sichuan, China, pp: 1-6.

75:  Afzal, M.H.B., S. Kabir and O. Sidek, 2012. An in-depth review: Structural health monitoring using fiber optic sensor. IETE Techn. Rev., 29: 105-113.
Direct Link  |  

76:  Friswell, M.I. and J.E.T. Penny 2002. Crack modeling for structural health monitoring. Struct. Health Monit., 1: 139-148.
CrossRef  |  Direct Link  |  

77:  Duan, W.H., Q. Wang and S.T. Quek, 2010. Applications of piezoelectric materials in structural health monitoring and repair: Selected research examples. Materials, 3: 5169-5194.
CrossRef  |  Direct Link  |  

78:  2017. Review on system development in eddy current testing and technique for defect classification and characterization. IET Circ. Devices Syst.
CrossRef  |  

79:  2016. Giant magnetoresistance sensors: A review on structures and non-destructive eddy current testing applications. Sensors, Vol. 16, No. 3.
CrossRef  |  

80:  2017. An eddy current testing platform system for pipe defect inspection based on an optimized eddy current technique probe design. Sensors, Vol. 17.
CrossRef  |  

81:  Rifai, D., N.A. Abdalla, N. Khamsah, K. Ali and R. Ghoni, 2015. Defect signal analysis for nondestructive testing. Proceedings of the FluidsChR, November 25-27, 2015, Langkawi, Malaysia -.

82:  2016. Subsurface defects evaluation using eddy current testing. Indian J. Sci. Technol., Vol. 9.
CrossRef  |  

83:  2017. Investigate of the effect of width defect on eddy current testing signals under different materials. Indian J. Sci. Technol., Vol. 10.
CrossRef  |  

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