Open Access
Issue
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
Article Number 02009
Number of page(s) 13
Section Smart Manufacturing and Industrial 4.0
DOI https://doi.org/10.1051/matecconf/201925502009
Published online 16 January 2019
  1. S.L. Bryant, The one-stop carbon solution an ingenious plan would bury carbon while providing fuel for electricity, Scientific American 309, 72–77 (2013) [CrossRef] [Google Scholar]
  2. L. Popoola, et al., Corrosion problems during oil and gas production and its mitigation, International Journal of Industrial Chemistry 4 (35) (2013) [CrossRef] [Google Scholar]
  3. J.W. Sowards, E. Mansfield, Corrosion of copper and steel alloys in a simulated underground storage-tank sump environment containing acid- producing bacteria, Corrosion Science 87, 460–471 (2014) [CrossRef] [Google Scholar]
  4. R.B. Rebak, Materials in nuclear waste disposition, Jom 66, 455–460 (2014) [CrossRef] [Google Scholar]
  5. C.H.D. Williamson, et al., Microbially influenced corrosion communities associated with fuel-grade ethanol environments, Applied Microbiology and Biotechnology 99, 6945–6957 (2015) [CrossRef] [Google Scholar]
  6. United States Environment Protection Agency (EPA), Preventing and detecting underground storage tank (UST) release (2015) Available: https://www.epa.gov/ust/preventing-and-detecting-underground-storage-tank-ust-releases [Google Scholar]
  7. B. DuBose, EPA finds moderate or severe corrosion in most underground diesel tanks (2016) Available: https://www.materialsperformance.com/articles/material/selectiondesign/2016/09/epa-finds-moderate-or-severe-corrosion-in-most-underground-diesel-tanks [Google Scholar]
  8. R. Kazys, R. Sanderson, Condition monitoring of large oil and chemical storage tanks using guided waves, BINDT annual Conference, Torquay, UK (2004) Available: http://www.twi-global.com/technical-knowledge/published-papers/condition-monitoring-of-large-oil-and-chemical-storage-tanks-using-guided-waves-september-2004/. [Google Scholar]
  9. Á. Angulo, S. Soua, Long range ultrasonic guided waves for pipeline inspection, COMSOL conference, Cambridge, UK (2014) [Google Scholar]
  10. J. Kanfoud, et al., New reliable technology for soil inspection using active vibration control in contaminated sites with drilling machinery condition monitoring, CM & MFPT, 1–13 (2015) [Google Scholar]
  11. Evironment Protection Agency (EPA), Release detection for underground storage tanks and piping: Straight talk on tanks (2016) Available: https://www.epa.gov/ust/release-detection-underground-storage-tanks-and-piping-straighttalk-tanks [Google Scholar]
  12. K. Damen, et al., Health, safety and environmental risks of underground CO2 storage overview of mechanisms and current knowledge, Climate Change 74, 289–318 (2006) [CrossRef] [Google Scholar]
  13. D.R. Petrolia, What have we learned from the deepwater horizon disaster? an economist perspective, Journal of Ocean and Coastal Economics 2014 (2015) [CrossRef] [Google Scholar]
  14. E. Buskey, et al., Impact of oil spills on marine life in the gulf of Mexico: Effect on plankton, nekton and deep-sea benthos, Oceanography 29, 174–181 (2016) [CrossRef] [Google Scholar]
  15. P.P. Povinec and K. Hirose, Fukushima radionuclides in the NW Pacific, and assessment of doses for Japanese and world population from ingestion of seafood, Sci. Rep. 5 (9016) (2015) [CrossRef] [Google Scholar]
  16. A.H. Gallardo and A. Marui, The aftermath of the Fukushima nuclear accident: Measures to contain groundwater contamination, Science of the Total Environment 547, 261–268 (2016) [CrossRef] [Google Scholar]
  17. J.I. Chang, C.-C. Lin, A study of storage tank accidents, Journal of Loss Prevention in the Process Industries 19, 51–59 (2006) [CrossRef] [Google Scholar]
  18. M. Burdumy, et al., One-second MRI of a three-dimension vocal tract to measure dynamic articulator modifications, Journal of Magnetic Resonance Imaging 46, 94–101 (2016) [CrossRef] [Google Scholar]
  19. J.G. Chen, et al., Video camera-based vibration measurement for civil infrastructure applications, Journal of Infrastructure Systems 23 (2017) [Google Scholar]
  20. J. Poley, M. Dines, Metallic wear debris sensors: promising developments in failure prevention for wind turbines gearsets and similar components, Industrial and Commercial Applications of Smart Structures Technologies (2011) [Google Scholar]
  21. S. Lunt, et al., Recent developments in online oil condition monitoring sensors and alignment with ASTM methods and practices, Journal od ASTM International 8 (103632) (2011) [Google Scholar]
  22. X. Yan, et al., Study of on-line condition monitoring and fault feature extraction for marine diesel engines based on tribological information, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 229, 291–300 (2014) [CrossRef] [Google Scholar]
  23. B. Yuan, et al., Eddy current thermography imaging for condition-based maintenance of overlay welded components under multi-degradation, Marine Structures 53, 136–147 (2017) [CrossRef] [Google Scholar]
  24. B.S. Prasad, et al., Condition Monitoring of tuning process using infrared thermography technique: an experimental approach, Infrared Physics & Technology 81, 137–147 (2017) [CrossRef] [Google Scholar]
  25. J. Dai. et al., Power fluctuation evaluation of large- scale wind turbine based on SCADA data, IET Renewable Power Generation 11, 395–402 (2017) [CrossRef] [Google Scholar]
  26. J. Tautz-Weinert, S.J. Watson, Using SCADA data for wind turbine condition monitoring: a review, IET Renewable Power Generation 11, 382–394 (2017) [CrossRef] [Google Scholar]
  27. C. Faure, et al., Empirical and fully bayesian approaches for the identification of vibration sources from transverse displacement measurements, Mechanical System and Signal Processing 94, 180–201 (2017) [CrossRef] [Google Scholar]
  28. W. Zhang, et al., Vibration response analysis of an elastic-support cantilever beam with crack and offset boundary, Mechnaical System and Signal Processing 95, 205–218 (2017) [CrossRef] [Google Scholar]
  29. K. Evans and P. Bedient, A transient methodology fo assessing risk: Development and comparison with the conventional approach, The Proceedings of the 1995 Petroleum Hydrocarbons and Organic Chemicals in Ground Water: Prevention, Detection, and Remediation. Conference and Exposition, Houston, Texas, 111–125 (1995) [Google Scholar]
  30. S. Bajic, et al., Analysis of underground storage tank waste simulants by fourier transform infrared photoacoustic spectroscopy, Applied Spectroscopy 49, 1000–1005 (1995) [CrossRef] [Google Scholar]
  31. L.V.D. Meer, Computer modelling of underground CO2 storage, Energy Conversion and Management 37, 1155–1160 (1996) [CrossRef] [Google Scholar]
  32. W. Daily, Electrical resistance tomography, The Leading Edge (2004) [Google Scholar]
  33. R Yumrutaş, et al., Computational model for a ground coupled space cooling system with an underground energy storage tank, Energy and Buildings 37, 353–360 (2005) [CrossRef] [Google Scholar]
  34. R. Sacile, Remote real-time monitoring and control of contamination in underground storage tank systems of petrol products, Journal of Cleaner Production 15 (13-14), 1295–1301 (2007) [CrossRef] [Google Scholar]
  35. C.-H. Moon, et al., Organically modified low-grade kaolin as a secondary containment material for underground storage tanks, Environmental Geochemistry and Health 29, 271–280 (2007) [CrossRef] [Google Scholar]
  36. K. Hatayama, Lessons from the 2003 Tokachi-Oki, Japan, earthquake for prediction of long-period, Journal of Seismology 12, 255–263 (2007) [CrossRef] [Google Scholar]
  37. F. Charpentier, et al., Infrared monitoring underground CO2 storage using chalcogenide glass fiber, Optical Materials 31 (3), 496–500 (2009) [CrossRef] [Google Scholar]
  38. F. Trebuňa, et al., Failure analysis of storage tank, Engineering Failure Analysis 16 (26–38) (2009) [CrossRef] [Google Scholar]
  39. J.-S. Kim, et al., A failure analysis of fillet joint cracking in an oil, Journal of Loss Prevention in the Process Industries 22, 845–849 (2009) [CrossRef] [Google Scholar]
  40. L. Sun, Y. Li, Large vertical storage tank bottom evaluation via acoustic emission signal analysis, Control and Decision Conference (CCDC), Chinese IEEE, Mianyang, China (2011) [Google Scholar]
  41. S.C.A. Lafortune, et al., First steps in coupling continous carbon isotopic measurements with already proven subsurface gas monitoring methods above underground carbon dioxide storage sites, Energy Procedia 4, 3526–3533 (2011) [CrossRef] [Google Scholar]
  42. G.I. Sarkandi, A Zabihollah, A computational model for health monitoring of storage tanks using fiber bragg grating optical fiber, Journal of Civil Structural Health Monitoring 1, 97–102 (2011) [CrossRef] [Google Scholar]
  43. R.T. Enander, et al., Reducing drinking water supply chemical contamination: Risks from underground, Risk analysis 32, 2182–2197 (2012) [CrossRef] [Google Scholar]
  44. B. Yousif, H. Ku, Suitability of using coir fiber/ploymer composite for the design of liquid storage, Material & Design 36, 847–853 (2012) [CrossRef] [Google Scholar]
  45. H. Fouli, Assessing the efficiency of palm-based insulators for storage water tanks under variable ambient temperatures, Arabian Journal for Science and Engineering 38, 1321–1332 (2013) [CrossRef] [Google Scholar]
  46. M. Bartholmai, et al., Multifunctional sensor for monitoring of CO2 underground storage by comprehensive and spatially resolved measuring of gas concentrations, temperature and structure changes, Energy Procedia 37, 4033–4040 (2013) [CrossRef] [Google Scholar]
  47. V. Mittal, et al., Dynamic analysis of liquid storage tank under blast using coupled eulerlagrange formulation, Thin-Walled Structures 84, 91–111 (2014) [CrossRef] [Google Scholar]
  48. D. Sanjuan-Delmás, et al., Environmental and geometric optimisation of cylindrical drinking water storage tanks, The Internatiobal Journal of Life Cycle Assessment 20, 1612–1624 (2014) [CrossRef] [Google Scholar]
  49. M.-E., Ts, et al., Remote real time monitoring for underground contamination in Mongolia using electrical impedance tomography, Journal of Nondestructive 35 (2015) [Google Scholar]
  50. A.A. Razak, et al., Mobile robot structure design, modelling and simulation for confined space, 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA), IEEE (2016) [Google Scholar]
  51. S. Periyannan, K. Balasubramaniam, Distributed temperature sensing using a spiral configuration, AIP Conference Proceedings 1806(1) (2016) [Google Scholar]
  52. G.P. Macey, et al., Air concentrations of volatile compounds near oil and gas production: a community based, Environmental Health 13 (2014) [CrossRef] [Google Scholar]
  53. D. Weber, F. Schwille, Passive vapor monitoring of underground storage tanks for leak detection, Environmental Monitoring and Assessment 16, 99–116 (1999) [CrossRef] [Google Scholar]
  54. G.A. Robbins, et al., Occurrence of MTBE in heating oil and diesel fuel in Connecticut, Ground Water Monitoring and Remediation 20, 82–86 (2000) [CrossRef] [Google Scholar]
  55. J. Wilson, et al., Monitored natural attenuation of MTBE as a risk management option at leaking undergroud storage tank sites, Environmental Protection Agency (2005) Available: https://nepis.epa.gov/Exe/ZyPDF.cgi/2001716 R.PDF?Dockey=20017I6R.PDF [Google Scholar]
  56. A.E. Kehew, P.M. Lynch, Concentration trends and water-level fluctuations at underground storage tank sites, Environmental Earth Sciences 62, 985–998 (2010) [CrossRef] [Google Scholar]
  57. T. Sairat, et al., Investigation of gasoline distributions within petrol stations: sptial and seasonal concentrations, sources, mitigation measures, and occupationally exposed symptoms, Environmental Science and Pollution Research 22, 13870–13880 (2015) [CrossRef] [Google Scholar]
  58. W.H. Huang, C.M. Kao, Bioremediation of petroleum-hydrocarbon contaminated groundwater under sulfate-reducing conditions: Effectiveness and mechanism study, Journal of Environmental Engineering 142(3) (2016) [Google Scholar]
  59. M. Balseiro-Romero, et al., Characterization and fingerprinting of soil and groundwater contamination sources around a fuel distribution station in Galicia (NW Spain), Environmental Monitoring and Assessment 188 (2016) [CrossRef] [Google Scholar]
  60. M.J. Metcalf, et al., Application of first order kinetics to characterize MTBE natural attenuation in groundwater, Journal of Contaminant Hydrology 187, 47–54 (2016) [CrossRef] [Google Scholar]
  61. K. Parmar, et al., Robust direct hydrocarbon sensor based on novel carbon nanotube nanocomposites for leakage detection, 11th International Pipeline Conference: Operations, Monitoring and Maintenance; Material and Joining. Vol. 3. Pipeline Division, ASME, Calgary, Alberta, Canada (2016) [Google Scholar]
  62. M. Andreolli, et al., Bioremediation of diesel contamination at an underground storage tank site: a spatial analysis of the microbial community, World Journal of Microbiology and Biotechnology 32 (2015) [Google Scholar]
  63. R.C.P. Minetti, et al., In situ chemical oxidation of btex and mtbe by ferrate: ph dependence and stability, Journal of Hazardous Materials 324, 448–456 (2017) [CrossRef] [Google Scholar]
  64. B. He, et al., A numerical simulation study on the formation and dispersion of flammable vapor cloud in underground confined space, Process Safety and Environmental Protection 107, 1–11 (2017) [CrossRef] [Google Scholar]
  65. I.M. Nambi, et al., An assessment of subsurface contamination of an urban coastal aquifer due to oil spill, Environmental Monitoring and Assessment 189, (2017) [CrossRef] [Google Scholar]
  66. D.M. Brown, et al., Comparison of landfarming amendments to improve bioremediation of petroleum hydrocarbons in niger delta soils, Science of The Total Environment 596-597, 284–292 (2017) [CrossRef] [Google Scholar]
  67. United States Environmental Protection Agency (EPA), Revising underground storage tank regulations - revisions to existing requirements and new requirements for secondary containment and operator training; final rule (2017) Available: https://www.epa.gov/ust/revising-underground-storage-tank-regulations-revisions-existing-requirements-and-new [Google Scholar]
  68. American Petroleum Institute (API), Pressure Vessel Inspection Code: In-Service Inspection, Rating, Repair, and Alteration (API 510) (2017) [Google Scholar]
  69. European Commission, Guidelines: Equipment and protective systems intended for use in potentially explosive atmospheres, (ATEX 2014/34/EU), Brussels, Belgium (2016) [Google Scholar]
  70. ASTM International, Standard Test Methods and Definitions for Mechanical Testing of Steel Products, (ASTM A370-17), West Conshohocken, PA (2017) [Google Scholar]
  71. British Standards Institute (BSI), Specification for the design and manufacture of site built, vertical, cylindrical, flat-bottomed, above ground, welded, steel tanks for the storage of liquids at ambient temperature and above (AMD Corrigendum 15597), (BS EN 14015:2004), London, UK (2004) [Google Scholar]
  72. The International Organization for Standardization (ISO), Specification and qualification of welding procedures for metallic materials Welding procedure test Part 1: Arc and gas welding of steels and arc welding of nickel and nickel alloys, (ISO 15614-1:2017), Berlin, Germany (2017) [Google Scholar]
  73. The International Organization for Standardization (ISO), Qualification testing of welders - Fusion welding, (ISO 9606-1:2012), Berlin, Germany (2012) [Google Scholar]
  74. A. Rytter, Vibrational based inspection of civil engineering structures, Ph.D. thesis, Dept. of Building Technology and Structural Engineering, Aalborg University (1993) [Google Scholar]
  75. E.P. Carden, Vibration based condition monitoring: A review, Structural Health Monitoring 3, 355–377 (2004) [CrossRef] [Google Scholar]
  76. M. Hillpert, et al., Hydrocarbon release during fuel storage and transfer at gas stations: Environmental and health effects, Current Environmental Health Reports 2, 412–422 (2015) [CrossRef] [Google Scholar]
  77. C.R. Farrar, S.W. Doebling, Damage detection and evaluation II, Modal Analysis and Testing, 363, Springer, Dordrecht (1999) [Google Scholar]
  78. A.B.A. Dawod, et al., On model selection for autocorrelated processes in statistical process control, Quality and Reliability Engineering International 33, 867–882 (2016) [CrossRef] [Google Scholar]
  79. Z. Li, et al., Robust object tracking based on adaptive templates matching via the fusion of multiple features, Journal of Visual Communication and Image Representation 44, 1–20 (2017) [CrossRef] [Google Scholar]
  80. Y. Jung, H. Kim, Detection of pvc by using a wavelet-based statistical ecg monitoring procedure, Biomedical Signal Processing and Control 36, 176–182 (2017) [CrossRef] [Google Scholar]
  81. W.K. Ngui, et al., Blade fault diagnosis using artificial neural network, International Journal of Applied Engineering Research 12(4), 519–526 (2017) [Google Scholar]
  82. V. K. Chillara, C. J. Lissenden, Review of nonlinear ultrasonic guided wave nondestructive evaluation: theory, numerics, and experiments, Optical Engineering 55 (1) (2015) [CrossRef] [Google Scholar]
  83. M. Eybpoosh, et al., Effects of damage location and size on sparse representation of guided-waves for damage diagnosis of pipelines under varying temperature, Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure (2015) [Google Scholar]
  84. J. Cheng, L.J. Bond, Assessment of ultrasonic NDT methods for high speed rail inspection, AIP Conference Proceedings, 1650 (1) (2015) [Google Scholar]
  85. N.K. Mutlib, et al., Ultrasonic health monitoring in structural engineering: buildings and bridges, Structural Control and Health Monitoring 23, 409–422 (2015) [CrossRef] [Google Scholar]
  86. P. Wilcox, et al., The effect of dispersion on long- range inspection using ultrasonic guided waves, NDT & E International 34, 1–9 (2001) [CrossRef] [Google Scholar]
  87. E. Leinov, et al., “Guided wave attenuation in pipes buried in sand,” Journal of Sound and Vibration 347, 96–114, 2015 [CrossRef] [Google Scholar]
  88. Z. Zhang, et al., The influence of different slopes of defects on ultrasonic guided-wave in flat steel, Advanced Research and Technology in Industry Applications (WARTIA), IEEE Workshop. IEEE, Ottawa, ON, Canada, 47–49 (2014) [Google Scholar]
  89. A.A. Pollock, Inspecting Bridge with Acoustic Emission, Guidelines prepared for the U.S. Department of Transportation and Federal Highway Administration (FHWA), Technical Report TR-103-126/95, Physical Acoustic Corp., Princeton Junction, NJ (1995) [Google Scholar]
  90. K.M. Holford, et al., A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding aerospace environments: An overview, Progress in Aerospace Sciences 90, 1–11 (2017) [CrossRef] [Google Scholar]
  91. J. Walsh, et al., Monitoring the condition of marine renewable energy devices through underwater acoustic emissions: Case study of a wave energy converter in Falmouth Bay, UK, Renewable Energy 102, 205–213 (2017) [CrossRef] [Google Scholar]
  92. R. Raišutis, et al., Ultrasonic guided wave-based testing technique for inspection of multi-wire rope structures, NDT & E International 62, 40–49 (2014) [CrossRef] [Google Scholar]
  93. M.K. Yücel, et al., An ultrasonic guided wave approach for the inspection of overhead transmission line cables, Applied Acoustics 122, 23–34 (2017) [CrossRef] [Google Scholar]
  94. S. Sharma, A. Mukherjee, Ultrasonic guided waves for monitoring the setting process of concretes with varying workabilities, Construction and Building Materials 72, 358–366 (2014) [CrossRef] [Google Scholar]
  95. B. Masserey, et al., High-frequency guided ultrasonic waves for hidden defect detection in multi-layered aircraft structures, Ultrasonics 54, 1720–1728 (2014) [CrossRef] [Google Scholar]
  96. O.M. Malinowski, Ultrasonic guided wave testing of finned tubing, ASME Pressure Vessels and Piping Conference: High-Pressure Technology; Rudy Scavuzzo Student Paper Competition and 23 rd Annual Student Paper Competition; ASME NDE Division. Vol. 5. Pressure Vessels and Piping Division, ASME, Boston, Massachusetts, USA (2015) [Google Scholar]
  97. C. Beggan, et al., Using acoustic emission to predict surface quality, The International Journal of Advanced Manufacturing Technology 15, 737–742 (1999) [CrossRef] [Google Scholar]
  98. K. Asamene, M. Sundaresan, Acoustic emission- based monitoring of surfaces subjected to friction, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security. Proc. SPIE (2012) [Google Scholar]
  99. S. Shahidan, et al., Damage classification in reinforced concrete beam by acoustic emission signal analysis, Construction and Building Materials 45, 78–86 (2013) [CrossRef] [Google Scholar]
  100. R. Ernst, et al., One sensor acoustic emission localization in plates, Ultrasonics 64, 139–150 (2016) [CrossRef] [Google Scholar]
  101. A.H. Muntakim, et al., Interpretation of acoustic field data for leak detection in ductile iron and copper water-distribution pipes, Journal of Pipeline Systems Engineering and Practice 8 (3) (2017) [CrossRef] [Google Scholar]
  102. B. Dykas, J. Harris, Acoustic emission characteristics of a single cylinder diesel generator at various loads and with a failing injector, Mechanical Systems and Signal Processing 93, 397–414 (2017) [CrossRef] [Google Scholar]
  103. M.K. Lim, H. Cao, Combining multiple NDT methods to improve testing effectiveness, Construction and Building Materials 38, 1310–1315 (2013) [CrossRef] [Google Scholar]
  104. M. Liu, et al., Applications of a nanocomposite- inspired in-situ broad- band ultrasonic sensor to acousto-ultrasonics-based passive and active structural health monitoring, Ultrasonics 78, 166–174 (2017) [CrossRef] [Google Scholar]
  105. T. Parthipan, et al., Long range ultrasonic inspection of aircraft wiring, 23 rd International Symposium on Industrial Electronics (ISIE), IEEE, Istanbul, Turkey (2014) [Google Scholar]
  106. M. Grewal, A. Andrews, Applications of kalman filtering in aerospace 1960 to the present [historical perspectives], IEEE Control Systems Magazine 30, 69–78 (2010) [CrossRef] [Google Scholar]
  107. S. Cuentas, et al., Support vector machine in statistical process monitoring: a methodological and analytical review, The International Journal of Advanced Manufacturing Technology 91, 2016 [Google Scholar]
  108. J.-G. Lee, et al., Deep learning in medical imaging: General overview, Korean Journal of Radiology 18 (4), 570–584 (2017) [CrossRef] [Google Scholar]
  109. Z. Ghahramani, Probabilistic machine learning and artificial intelligence, Nature 521, 452–459 (2015) [CrossRef] [Google Scholar]
  110. T. Hastie, et al., Chapter 7: Model Assessment and Selection, The elements of statistical learning: data mining, inference, and prediction, 2nd Edition, Springer (2009) [Google Scholar]
  111. A.P.D. Silva, Optimization approaches to supervised classification, European Journal of Operational Research 261, 772–788 (2017) [CrossRef] [Google Scholar]
  112. M.T. Hagh, et al., Fault classification and location of power transmission lines using artificial neural network, International Power Engineering Conference (IPEC), Singapore, 1109–1114 (2007) [Google Scholar]
  113. M. Mousavi, K. Butler-Purry, A novel condition assessment system for underground distribution applications, IEEE Transactions on Power Systems 24 (3), 1115–1125 (2010) [CrossRef] [Google Scholar]
  114. L.H. Lee, et al., An enhanced support vector machine classification framework by using euclidean distance function for text document categorization, Applied Intelligence 37, 80–99 (2011) [CrossRef] [Google Scholar]
  115. R.A. Francis, et al., Bayesian belief networks for predicting drinking water distribution system pipe breaks, Reliability Engineering & System Safety 130, 1–11 (2014) [CrossRef] [Google Scholar]
  116. L. Chen, et al., An ultrasonic guided wave signal processing and pattern recognition tool for studying the discontinuity axial length, Materials Evaluation 75 (5) (2017) [Google Scholar]
  117. K. Sepahvand, Stochastic finite element method for random harmonic analysis of composite plates with uncertain modal damping parameters, Journal of Sound and Vibration 400, 1–12 (2017) [CrossRef] [Google Scholar]
  118. H. Dai, et al., Nonlinear system stochastic response determination via fractional equivalent linearization and Karhunen-Lòeve expansion, Communications in Nonlinear Science and Numerical Simulation 49, 145–158 (2017) [CrossRef] [Google Scholar]
  119. J. Macías-Díaz, J. Villa-Morales, A deterministic model for the distribution of the stopping time in a stochastic equation and its numerical solution, Journal of Computational and Applied Mathematics 318, 93–106 (2017) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.