Current issue
Archive
About the Journal
About
Editorial Board
Abstract & Indexing
Contact
Editorial Policies
Authorship & COI
Ethical principles
Principles of Transparent Checklist
Open access
For Authors
Instructions to Authors
Detailing Guidelines for Authors
Review process
Review sheet
How to submit
Open Access license
ORIGINAL PAPER
An exploration of vibration based damage detection techniques for composite materials
1
Mechanical Engineering, Amrutvahini College of Engineering, Sangamner.
2
Professor, Mechanical Engineering, Sandip Institute of Technology and Research Center, Nashik, India
3
Professor, Mechanical Engineering, Late Sau. Kashibai Bhavarlalji Jain College of Engineering, Chandwad, Nashik, India
4
Department of Mechanical Engineering, Amrutvahini College of Engineering, Sangamner, SPPU, Pune, India
5
Associate Professor, Mechanical Engineering, Amrutvahini College of Engineering, Sangamner.
Submission date: 2024-09-27
Final revision date: 2024-11-28
Acceptance date: 2025-03-07
Online publication date: 2025-09-02
Publication date: 2025-09-02
Corresponding author
International Journal of Applied Mechanics and Engineering 2025;30(3):97-113
KEYWORDS
Composite materialsDamage detectionVibration analysisStructural health monitoringArtificial Neural Network
TOPICS
ABSTRACT
Structural damage monitoring is inevitable for the structures to perform during their intended service life adroitly. In the present review, literature related to techniques for diagnosing vibration-intensive damages have been evaluated in order to determine the material characteristics, such as stiffness and damping. Also, extensive review has been presented in the for damage detection in composite materials. The review encompasses the literature published in last 42 years, i.e., 1982 to 2024. The literature review is classified into sections as damage detection workflow, composite materials, damage detection techniques, and advanced damage detection techniques. The usage of strain energy, mode-shapes, waveform dimension, wavelet transform and updating finite element models in detection of damage are also discussed. Further, an overview of concepts, techniques, and advancement in vibration-induced damage detection are presented. The limitations of each technique are explained. An insight on advanced techniques and tools from genetic algorithm and artificial neural network regarding their employability to detect the damage is provided. This work portrays the damage detection methodologies.
REFERENCES (100)
1.
Matthews F. (1999): Damage in fiber-reinforced plastics; its nature, consequences and detection.– Key Engineering Materials, Trans. Tech. Publ., vol.167-168, pp.1-16.
2.
Sohn H. (2002): A Review of Structural Health Review of Structural Health Monitoring Literature 1996-2001.– Los Alamos National Laboratory.
3.
Abbas M. and Shafiee M. (2018): Structural health monitoring (SHM) and determination of surface defects in large metallic structures using ultrasonic guided waves.– Sensors, vol.18, No.11, pp.3958.
4.
Singh T. and Sehgal S. (2022): Damage identification using vibration monitoring techniques.– Materials Today: Proceedings, vol.69, No.4, DOI:10.1016/j.matpr.2022.08.204.
5.
Alarifi I.M., Alharb A. and Khan W.S. (2015): Thermal, electrical and surface hydrophobic properties of electrospun polyacrylonitrile nanofibers for structural health monitoring.– Materials, vol.8, No.10, pp.7017-7031.
6.
Alokita S. and Verma R. (2019): 4-Recent advances and trends in structural health monitoring.– Structural health monitoring of biocomposites, fibre-reinforced composites and hybrid composites.– pp.53-73, https://doi.org/10.1016/B978-0....
7.
Arsenault T.J., Achuthan A. and Marzocca P. (2013): Development of a FBG based distributed strain sensor system for wind turbine structural health monitoring.– Smart Materials and Structures, vol.22, No.7, pp.075027, DOI:10.1088/0964-1726/22/7/075027.
8.
Bagavathiappan S., Lahiri B.B. and Saravanan T. (2013): Infrared thermography for condition monitoring - a review.– Infrared Physics & Technology, vol.60, pp.35-55.
9.
De Castro B.A. and Baptista F.G. (2019): New signal processing approach for structural health monitoring in noisy environments based on impedance measurements.– Measurement, vol.137, pp.155-167.
10.
Dong C.-Z. and Catbas F.N. (2021): A review of computer vision-based structural health monitoring at local and global levels.– Structural Health Monitoring, vol.20, No.2, pp.692-743.
11.
De Medeiros R., Sartorato M. and Leite Ribeiro M. (2012): Numerical and experimental analyses about SHM metrics using piezoelectric materials.– in International Conference on Noise and Vibration Engineering (ISMA2012), Leuven, Belgium.
12.
Rabelo D., Valder S.Jr. and Mendes F.N.R. (2017): Impedance-based structural health monitoring and statistical method for threshold-level determination applied to 2024-T3 aluminum panels under varying temperature.– Structural Health Monitoring, vol.16, No.4, pp.365-381.
13.
Di Sante R. (2015): Fibre optic sensors for structural health monitoring of aircraft composite structures: recent advances and applications.– Sensors, vol.15, No.8, pp.18666-18713.
14.
Das M. and Sahu S. (2021): Composite materials and their damage detection using AI techniques for aerospace application: a brief review.– Materials Today: Proceedings, vol.44, pp. 955-960.
15.
Hassani S. and Mousavi M.(2021): Structural health monitoring in composite structures: a comprehensive review.– Sensors, vol.22, No.1, pp.153.
16.
Montalvao D. and Maia N.M.M. (2006): A review of vibration-based structural health monitoring with special emphasis on composite materials.– Shock and Vibration Digest, vol.38, No.4, pp.295-324.
17.
Russo A. and Palumbo C. (2023): The role of intralaminar damages on the delamination evolution in laminated composite structures.– Heliyon, vol.9, No.4, e15060.
18.
Aoki R., Higuchi R. and Yokozeki T. (2021): Damage-mechanics mesoscale modeling of composite laminates considering diffuse and discrete ply damages: effects of ply thickness.– Composite Structures, vol.277, pp.114609.
19.
Nayak C., Kushram P. and Ali M. (2023): Multi-length scale strengthening and cytocompatibility of ultra high molecular weight polyethylene bio-composites by functionalized carbon nanotube and hydroxyapatite reinforcement.– Journal of the Mechanical Behavior of Biomedical Materials, vol.140, pp.105694.
20.
Devaraju S. and Alagar M. (2021): Polymer matrix composite materials for aerospace applications.– Encyclopedia of Materials: Composites,vol.1, pp.947-969, DOI-10.1016/B978-0-12-819724-0.00052-5.
21.
Harussani M., Sapuan S.M. and Nadeem G. (2022): Recent applications of carbon-based composites in defense industry: A review.– Defense Technology, vol.18, No.8, pp.1281-1300, DOI-10.1016/j.dt.2022.03.006.
22.
Muhammad A., Rahman Md.R., Baini R. and Bakri M.K.B. (2021): Applications of sustainable polymer composites in automobile and aerospace industry, in Advances in sustainable polymer composites.– Woodhead Publishing Series in Composites Science and Engineering, Elsevier, pp.185-207, https://doi.org/10.1016/B978-0....
23.
Fan W. and Qiao P. (2011): Vibration-based damage identification methods: a review and comparative study.– Structural Health Monitoring, vol.10, No.1, pp.83-111.
24.
Sazonov E. and Klinkhachorn P. (2005): Optimal spatial sampling interval for damage detection by curvature or strain energy mode shapes.– Journal of Sound and Vibration, vol.285, No.4-5, pp.783-801.
25.
Cao M. and Qiao P. (2009): Novel Laplacian scheme and multiresolution modal curvatures for structural damage identification.– Mechanical Systems and Signal Processing, vol.23, No.4, pp.1223-1242.
26.
Maeck J., Wahab M.A. and Peeters B. (2000): Damage identification in reinforced concrete structures by dynamic stiffness determination.– Engineering Structures, vol.22, No.10, pp.1339-1349.
27.
Xu Y., Chen D. and Zhu W.D. (2014): Identification of embedded horizontal cracks in beams using measured mode shapes.– Journal of Sound and Vibration, vol.333, No.23, pp.6273-6294.
28.
Shi Z., Law S., and Zhang L. (1998): Structural damage localization from modal strain energy change.– Journal of Sound and Vibration, vol.218, No.5, pp.825-844.
29.
Dewangan P., Parey A., Hammami A., Chaari F. and Haddar M. (2020): Damage detection in wind turbine gearbox using modal strain energy.– Engineering Failure Analysis, vol.107, pp.104228, https://doi.org/10.1016/j.engf....
30.
Nick H., Aziminejad A., Hosseini M. and Laknejadi K. (2021): Damage identification in steel girder bridges using modal strain energy-based damage index method and artificial neural network.– Engineering Failure Analysis, vol.119, pp.105010.
31.
Alavinezhad M., Ghodsi M., Ketabdari M. J. and Nekooei M. (2022): Numerical and experimental structural damage detection in an offshore flare bridge using a proposed modal strain energy method.– Ocean Engineering, vol.252, pp.111055.
32.
Douka E., Loutridis S. and Trochidis A. (2003): Crack identification in beams using wavelet analysis.– International Journal of Solids and Structures, vol.40, No.13-14, pp.3557-3569.
33.
Rucka M. and Wilde K. (2006): Application of continuous wavelet transform in vibration based damage detection method for beams and plates.– Journal of Sound and Vibration, vol.297, No.3-5, pp.536-550.
34.
Saadatmorad M., Jafari-Talookolaei R.A., Pashai M.H. and Khatir S. (2021): Damage detection on rectangular laminated composite plates using wavelet based convolutional neural network technique.– Composite Structures, vol.278, pp.114656.
35.
Vamsi I., Hemanth M.P., Penumakala P.K. and Sabareesh G.R. (2022): Damage monitoring of pultruded GFRP composites using wavelet transform of vibration signals.– Measurement, vol.195, pp.111177.
36.
Jahangir H., Hasani H. and Md. Esfahani R. (2021): Wavelet-based damage localization and severity estimation of experimental RC beams subjected to gradual static bending tests.– Structures, vol.34, pp.3055-3069.
37.
Zhou K., Lei D, He J. Zhang P. Bai P. and Zhu F. (2021): Real-time localization of micro-damage in concrete beams using DIC technology and wavelet packet analysis.– Cement and Concrete Composites, vol.123, pp.104198.
38.
Zhu H., Li J., Tian W. and Weng S. (2021): An enhanced substructure-based response sensitivity method for finite element model updating of large-scale structures.– Mechanical Systems and Signal Processing, vol.154, pp.107359.
39.
Nozari A., Iman B., Seyedsina Y. and Moaveni B. (2017): Effects of variability in ambient vibration data on model updating and damage identification of a 10-story building.– Engineering Structures, vol.151, pp.540-553.
40.
Mishra S., Vanli O.A., Alduse B. and Jung S. (2017): Hurricane loss estimation in wood-frame buildings using Bayesian model updating: Assessing uncertainty in fragility and reliability analyses.– Engineering Structures, vol.135, pp.81-94.
41.
Tiachacht S., Khatir S., Cuong L.T. and Rao R.V. (2021): Inverse problem for dynamic structural health monitoring based on slime mould algorithm.– Engineering with Computers, vol.38, pp.2205-2208.
42.
Al Thobiani F., Khatir S., Benaissa B. and Ghandourah E.I. (2022): A hybrid PSO and grey wolf optimization algorithm for static and dynamic crack identification.– Theoretical and Applied Fracture Mechanics, vol.118, pp.103213.
43.
Gudmundson P. (1982): Eigen frequency changes of structures due to cracks, notches or other geometrical changes.– Journal of the Mechanics and Physics of Solids, vol.30, No.5, pp.339-353.
44.
Hu H.B. and Wang B.-T. and Su J.-S. (2004): Application of modal analysis to damage detection in composite laminates.– in Engineering Systems Design and Analysis, pp.85-91, https://doi.org/10.1115/ESDA20....
45.
Govindasamy M., Kamalakannan G., Kesavan C. and Meenashisundaram G. (2020): Damage detection in glass/epoxy laminated composite plates using modal curvature for structural health monitoring applications.– Journal of Composites Science, vol.4, No.4, pp.185.
46.
Pandey A. and Biswas M. (1994): Damage detection in structures using changes in flexibility.– Journal of Sound and Vibration, vol.169, No.1, pp.3-17.
47.
Ratcliffe C.P. (1997): Damage detection using a modified Laplacian operator on mode shape data.– Journal of Sound and Vibration, vol.204, No.3, pp.505-517.
48.
Ren W.-X. and De Roeck G. (2002): Structural damage identification using modal data. I: Simulation verification.– Journal of Structural Engineering, vol.128, No.1, pp.87-95.
49.
Wong C., Zhu W. and Zhu G.Y. (2004): On an iterative general-order perturbation method for multiple structural damage detection.– Journal of Sound and Vibration, vol.273, No.1-2, pp.363-386.
50.
Rahai A., Bakhtiari‐Nejad F. and Esfandiari A. (2007): Damage assessment of structure using incomplete measured mode shapes.– Structural Control and Health Monitoring, The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures, vol.14, No.5, pp.808-829, https://doi.org/10.1002/stc.18....
51.
Lakhdar M, Mohammed D., Boudjemâa L. and Rabiâ A. (2013): Damages detection in a composite structure by vibration analysis.– Energy Procedia, vol.36, pp.888-897.
52.
Kyriazoglou C. and Le Page B. (2004): Vibration damping for crack detection in composite laminates.– Composites Part A: Applied Science and Manufacturing, vol.35, No.7-8, pp.945-953.
53.
Yan Y.J. and Yam L.H. (2004): Detection of delamination damage in composite plates using energy spectrum of structural dynamic responses decomposed by wavelet analysis.– Computers & Structures, vol.82, No.4-5, pp.347-358.
54.
Mian A., Han X., Islam S. and Newaz G. (2004): Fatigue damage detection in graphite/epoxy composites using sonic infrared imaging technique.– Composites Science and Technology, vol.64, No.5, pp.657-666.
55.
Li H., Weis M., Herszberg I. and Mouritz A.P. (2004): Damage detection in a fibre reinforced composite beam using random decrement signatures.– Composite Structures, vol.66, No.1-4, pp.159-167.
56.
Caccese V. and Mewer R. (2004): Detection of bolt load loss in hybrid composite/metal bolted connections.– Engineering Structures, vol.26, No.7, pp.895-906.
57.
Ratcliffe C., Heider D., Crane R., Krauthauser C., Yoon M.K. and Gillespie Jr. J.W. (2008): Investigation into the use of low cost MEMS accelerometers for vibration based damage detection.– Composite Structures, vol.82, No.1, pp.61-70.
58.
Yang Z., Wang L., Wang H. and Ding Y. (2009): Damage detection in composite structures using vibration response under stochastic excitation.– Journal of Sound and Vibration, vol.325, No.4, pp.755.
59.
Hu H. and Wang J. (2009): Damage detection of a woven fabric composite laminate using a modal strain energy method.– Engineering Structures, vol.31, No.5, pp.1042-1055.
60.
Jena P., Thatoi D.N., Nanda J. and Parhi D.R.K. (2012): Effect of damage parameters on vibration signatures of a cantilever beam.– Procedia Engineering, vol.38, pp.3318-3330.
61.
Herman A. and Orifici A. (2013): Vibration modal analysis of defects in composite T-stiffened panels.– Composite Structures, vol.104, pp.34-42.
62.
Esmaeel R.A. and Taheri F. (2012): Delamination detection in laminated composite beams using the empirical mode decomposition energy damage index.– Composite Structures, vol.94, No.5, pp.1515-1523.
63.
Na S. and Lee H.K. (2012): Resonant frequency range utilized electro-mechanical impedance method for damage detection performance enhancement on composite structures.– Composite Structures, vol.94, No.8, pp.2383-2389.
64.
Saponara V.L., Brandli C., Arronche L. and Lestari W. (2014): Gabor wavelet transform contours for the detection of uniaxial tensile damage in woven fiberglass/epoxy composites.– Mechanics Research Communications, vol.62, pp.138-145.
65.
Perez M.A. and Gil L. (2014): Impact damage identification in composite laminates using vibration testing.– Composite Structures, vol.108, pp.267-276.
66.
Anderson S., Aram P., Bhattacharya B. and Kadirkamanathan V. (2014): Analysis of composite plate dynamics using spatial maps of frequency-domain features described by Gaussian processes.– IFAC Proceedings, vol.47, No.1, pp.949-954.
67.
Vo-Duy T., Ho-Huu V., Dang-Trung H. and Nguyen-Thoi T. (2016): A two-step approach for damage detection in laminated composite structures using modal strain energy method and an improved differential evolution algorithm.– Composite Structures, vol.147, pp.42-53.
68.
Pieczonka Ł., Ambroziński Ł., Staszewski W.J., Barnoncel D. and Pérès P. (2017): Damage detection in composite panels based on mode-converted Lamb waves sensed using 3D laser scanning vibrometer.– Optics and Lasers in Engineering, vol.99, pp.80-87.
69.
Geweth C.A. and Khosroshahi F.S. (2017): Damage detection of fibre-reinforced composite structures using experimental modal analysis.– Procedia Engineering, vol.199, pp.1900-1905.
70.
Porcu M.C., Pieczonka Ł. and Frau A. (2017): Assessing the scaling subtraction method for impact damage detection in composite plates.– Journal of Nondestructive Evaluation, vol.36, No.2, pp.1-16.
71.
De Fenza A., Petrone G., Pecora R. and Barile M. (2017): Post-impact damage detection on a winglet structure realized in composite material.– Composite Structures, vol.169, pp.129-137.
72.
De Menezes V.G., Souza G.S.C., Vandepitte D., Tita V. and De Medeiros R. (2021): Defect and damage detection in filament wound carbon composite cylinders: a new numerical-experimental methodology based on vibrational analyses.– Composite Structures, vol.276, pp.114548.
73.
Pacheco-Chérrez J. and Cárdenas D. (2021): Experimental detection and measurement of crack-type damage features in composite thin-wall beams using modal analysis.– Sensors, vol.21,No.23, pp.8102.
74.
Hassani S. and Mousavi M. (2022): Damage detection of composite laminate structures using VMD of FRF contaminated by high percentage of noise.– Composite Structures, vol.286, pp.115243.
75.
Loi G., Uras N., Porcu M.C. and Aymerich F. (2022): Damage detection in composite materials by flexural dynamic excitation and accelerometer-based acquisition.– in IOP Conference Series: Materials Science and Engineering. IOP Publishing, DOI 10.1088/1757-899X/1214/1/012007.
76.
Wei Z. and Yam L. (2004): Detection of internal delamination in multi-layer composites using wavelet packets combined with modal parameter analysis.– Composite Structures, vol.64, No.3-4, pp.377-387.
77.
Wang Y. and Liang M. (2014): Damage detection method for wind turbine blades based on dynamics analysis and mode shape difference curvature information.– Mechanical Systems and Signal Processing, vol.48, No.1-2, pp.351-367.
78.
Pan J. and Zhang Z. (2019): A novel method of vibration modes selection for improving accuracy of frequency-based damage detection.– Composites Part B: Engineering, vol.159, pp.437-446.
79.
Raut N.P. and Kolekar A. (2021): Optimization techniques for damage detection of composite structure: A review.– Materials Today: Proceedings, vol.45, pp.4830-4834.
80.
Gillich G.-R., Furdui H., Wahab M.A. and Korka Z.-I. (2019): A robust damage detection method based on multi-modal analysis in variable temperature conditions.– Mechanical Systems and Signal Processing, vol.115, pp.361-379.
81.
Zhang Z., Zhan C. and Shankar K. (2017): Sensitivity analysis of inverse algorithms for damage detection in composites.– Composite Structures, vol.176, pp.844-859.
82.
Kahya V. Şimşek S. and Toğan V.(2022): Vibration-based damage detection in anisotropic laminated composite beams by a shear deformable finite element and harmony search optimization.– Research Square, DOI: 10.21203/rs.3.rs-1681275/v1.
83.
Han Y., Kumon R. and Baqersad J. (2022): Nondestructive evaluation of carbon-fiber composites using digital image correlation, acoustic emission, and optical based modal analysis.– Wind Engineering, vol.46, No.5, DOI: 0309524X221095206.
84.
Zacharakis I. and Giagopoulos D. (2022): Vibration-based damage detection using finite element modeling and the metaheuristic particle swarm optimization algorithm.– Sensors, vol.22, No.14, pp.5079.
85.
Loi G. and Aymerich F. (2022): Influence of sensor position and low-frequency modal shape on the sensitivity of vibro-acoustic modulation for impact damage detection in composite materials.– Journal of Composites Science, vol.6, No.7, pp.190.
86.
An H. and Youn B.D. (2022): A methodology for sensor number and placement optimization for vibration-based damage detection of composite structures under model uncertainty.– Composite Structures, vol.279, pp.114863.
87.
Shabani P. and Shabani. N. (2022): Fatigue life prediction of high-speed composite craft under slamming loads using progressive fatigue damage modeling technique.– Engineering Failure Analysis, vol.131, pp.105818.
88.
Saadatmorad M. Jafari-Talookolaei R.A and Pashai M.H. (2022): Pearson correlation and discrete wavelet transform for crack identification in steel beams.– Mathematics, vol.10, No.15, pp.2689.
89.
Ho L.V., Nguyen D.H., Mousavi M. and De Roeck G. (2021): A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks.– Computers & Structures, vol.252, pp.106568.
90.
Minh H.-L., Sang-To T., Wahab M.A. and Cuong-Le T. (2022): A new metaheuristic optimization based on K-means clustering algorithm and its application to structural damage identification.– Knowledge-Based Systems, vol.251, pp.109189.
91.
João C., Queiroz S. Ygor T. and Santos B. (2021): Damage detection in composite materials using tap test technique and neural networks.– Journal of Nondestructive Evaluation, vol.40, No.1, pp.1-9.
92.
Dabetwar S. and Ekwaro-Osire S. (2020): Damage detection of composite materials using data fusion with deep neural networks.– in Turbo Expo: Power for Land, Sea, and Air. American Society of Mechanical Engineers. DOI: 10.1115/GT2020-15097.
93.
Woo Y.J. (2022): Vibration based damage detection method with various boundary conditions using deep learning: a comparative study of experiments and FEA.– Hanyang University, Theses (Master).
94.
Reis P.A., Kelvin M. Iwasaki K. and De Medeiros R. (2022): Damage detection of composite beams using vibration response and artificial neural networks.– Proceedings of the Institution of Mechanical Engineers, Journal of Materials: Part L: Design and Applications, vol.236, No.7, pp.1419-1430.
95.
Saadatmorad M. and Jafari-Talookolaei R.A (2022): Application of multilayer perceptron neural network for damage detection in rectangular laminated composite plates based on vibrational analysis.– in Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment, Springer, pp.167-178, DOI:10.1007/978-981-16-7216-3_13.
96.
Maurya M., Sadarang J., Panigrahi I. and Das D. (2022): Detection of delamination in carbon fibre reinforced composite using vibration analysis and artificial neural network.– Materials Today: Proceedings, vol.49, pp.517-522.
97.
Mwambegu M.N. and Gnanamoorthy R. (2023): Water absorption in alkaline-treated coir pith - for use as reinforcement material in polymer matrix composites.– Materials Today: Proceedings, https://doi.org/10.1016/j.matp....
98.
Huang T. and Bobyr M. (2023): A Review of Delamination Damage of Composite Materials.– Journal of Composite Science, vol.7, No.11, p.468, https://doi.org/10.3390/jcs711....
99.
Kumar R.S. (2013): Analysis of coupled ply damage and delamination failure processes in ceramic matrix composites.– Acta Materialia, vol.61, No.10, pp.3535-3548, https://doi.org/10.1016/j.acta....
100.
Barshikar R.R. and Baviskar P. (2024): Evaluation of performance of vibration signatures for condition monitoring of worm gearbox by using ANN.– International Journal on Interactive Design and Manufacturing (IJIDeM), vol.18, No.10, pp.7291-7304, DOI https://doi.org/10.1007/s12008....
| eISSN: | 2353-9003 |
| ISSN: | 1734-4492 |
Task: edition of a scientific journal, its internationalization and ensuring open access to the journal via the Internet
© 2006-2025 Journal hosting platform by Bentus
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
