Structural Health Monitoring as a Big-Data Problem

References

• Santos JP. Smart structural health monitoring techniques for novelty identification in civil engineering structures. PhD Thesis, Instituto Superior Técnico – University of Lisbon, 2014.
   Google Scholar
• Cury A. Téchniques D’Anormalité Appliquées a la Surveillance de Santé Structurale. Université Paris-Est, Champs-sur-Marne, 2010.
   Google Scholar
• Mata J. Structural Safety Control of Concrete Dams Aided by Autmated Monitoring Systems. Univerisidade de Lisboa, Lisbon, 2013.
   Google Scholar
• Doebling SW, Farrar CR, Prime MB, Shevitz DW. Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review. Los Alamos, Los Alamos National Laboratory, NM, 1996.
   Google Scholar
• Worden K, Staszewski WJ, Hensman JJ. Natural computing for mechanical systems research: A tutorial overview. Mech. Syst. Signal Process. 2011; 25(1): 4–111. doi: 10.1016/j.ymssp.2010.07.013
   Web of Science ®Google Scholar
• Magalhães F. Operational Modal Analysis for Testing and Monitoring of Bridges and Special Structures. University of Porto, Faculty of Engineering, Porto, 2010.
   Google Scholar
• Santos J, Mata J, Cremona C, Silveira P. Detection of structural changes using symbolic representations of stochastic subspace identification estimates. In 8th Workshop on Structural Health Monitoring, Bilbao, 2016: 1–10.
   Google Scholar
• Bell G, Hey T, Szalay A. Beyond the data deluge. Science. 2009; 323(5919): 1297–1298. doi: 10.1126/science.1170411
   PubMed Web of Science ®Google Scholar
• Faniel IM, Zimmerman A. Beyond the data deluge : A research agenda for large-scale data sharing and reuse. Int. J. Digit. Curation. 2011; 6(1): 58–69. doi: 10.2218/ijdc.v6i1.172
   Google Scholar
• Sarkar S, Reddy KK, Giering M, Gurvich MR. Deep learning for structural health monitoring: A damage characterization application. In Annual Conference of the Prognostics and Health Management Society 2016, Denver, CO: PHM Society, 2016: 1–7.
   Google Scholar
• Fonseca A, Cabral B. Prototyping a GPGPU neural network for deep-learning Big data analysis. Big Data Res. 2017; 8: 50–56. doi: 10.1016/j.bdr.2017.01.005
   Web of Science ®Google Scholar
• Cha Y-J, Choi W, Büyüköztürk O. Deep learning-based crack damage detection using convolutional neural networks. Comput. Civ. Infrastruct. Eng. 2017; 32(5): 361–378. doi: 10.1111/mice.12263
   Web of Science ®Google Scholar
• Salazar F. A Machine Learning Based Methodology for Anomaly Detection in Dam Behaviour. Universitat Politécnica de Catalunya, Barcelona, 2016.
   Google Scholar
• Salazar F, Toledo MÁ, González JM, Oñate E. Early detection of anomalies in Dam performance: A methodology based on boosted regression trees. Struct. Control Heal. Monit. 2017; 24(11): 1–16.
   Web of Science ®Google Scholar
• Wu Z, Chin OB. From Big data to data science: A multi-disciplinary perspective. Big Data Res. 2014; 1: 1–1. doi: 10.1016/j.bdr.2014.08.002
   Google Scholar
• Jagadish HV. Big data and science: Myths and reality. Big Data Res. 2015; 2(2): 49–52. doi: 10.1016/j.bdr.2015.01.005
   Web of Science ®Google Scholar
• Laney D. 3D data management: Controlling data volume, velocity and variety. META Group: Stamford, CT. 2001.
   Google Scholar
• Hilbert M. Big data for development: A review of promises and challenges. Dev. Policy Rev. 2016; 34(1): 135–174. doi: 10.1111/dpr.12142
   Web of Science ®Google Scholar
• Huang T, Lan L, Fang X, An P, Min J, Wang F. Promises and challenges of Big data computing in health sciences. Big Data Res. 2015; 2(1): 2–11. doi: 10.1016/j.bdr.2015.02.002
   Web of Science ®Google Scholar
• Brunswicker S, Bertino E, Matei S. Big data for open digital innovation – A research roadmap. Big Data Res. 2015; 2(2): 53–58. doi: 10.1016/j.bdr.2015.01.008
   Web of Science ®Google Scholar
• Cury A, Crémona C. Assignment of structural behaviours in long-term monitoring: Application to a strengthened railway bridge. Struct. Heal. Monit. 2012; 11(4): 422–441. doi: 10.1177/1475921711434858
   Web of Science ®Google Scholar
• Santos JP, Cremona C, Silveira P, Calado L. Real-Time damage detection based on pattern recognition. Struct. Concr. 2016; 17(3): 338–354. doi: 10.1002/suco.201500092
   Web of Science ®Google Scholar
• Santos J, Crémona C, Calado L, Silveira P, Orcesi A. On-Line unsupervised detection of early damage. Struct. Control Heal. Monit. 2015; 23(7): 1047–1069. doi: 10.1002/stc.1825
   Web of Science ®Google Scholar
• Glaser SD, Tolman A. Sense of sensing: From data to informed decisions for the built environment. J. Infrastruct. Syst. 2008; 14(1): 4–14. doi: 10.1061/(ASCE)1076-0342(2008)14:1(4)
   Web of Science ®Google Scholar
• Posenato D. Model-Free Data Interpretation for Continuous Monitoring of Complex Structures. École Polytechnique Fédérale de Lausanne, Lausanne, 2009.
   Google Scholar
• Zhou HF, Ni YQ, Ko JM. Constructing input to neural networks for modelling temperature-caused modal variability: Mean temperatures, effective temperatures, and principal components of temperatures. Eng. Struct. 2010; 32(6): 1747–1759. doi: 10.1016/j.engstruct.2010.02.026
   Web of Science ®Google Scholar
• Cury A, Crémona C. Pattern recognition of structural behaviors based on learning algorithms and symbolic data concepts. Struct. Control Heal. Monit. 2012; 19(2): 161–186. doi: 10.1002/stc.412
   Web of Science ®Google Scholar
• Santos J, Silveira P. A SHM framework comprising real time data validation. In IALCCE 2012 – 3rd International Symposium on Life Cycle Civil Engineering, vol. ii, Abingdon: Taylor & Francis, 2012.
   Google Scholar
• Tang N. Big data cleaning. Web Technol. Appl. 2014; 8709: 13–24.
   Google Scholar
• Rousseuw P, Driessen K. A fast algorithm for the minimum covariance determinant estimator. J. Am. Stat. Assoc. 1999; 41(3): 212–223.
   Google Scholar
• Cury A, Cremona C, Dumoulin J. Long-Term monitoring of a PSC Box girder bridge: Operational modal analysis, data normalization and structural modification assessment. Mech. Syst. Signal Process. 2012; 33: 13–37. doi: 10.1016/j.ymssp.2012.07.005
   Web of Science ®Google Scholar
• Santos J, Crémona C, Orcesi AD, Silveira P. Multivariate statistical analysis for early damage detection. Eng. Struct. 2013; 56: 273–285. doi: 10.1016/j.engstruct.2013.05.022
   Web of Science ®Google Scholar
• Mata J. Interpretation of concrete Dam behaviour with artificial neural network and multiple linear regression models. Eng. Struct. 2011; 33(3): 903–910. doi: 10.1016/j.engstruct.2010.12.011
   Web of Science ®Google Scholar
• Cury A, Crémona C, Diday E. Application of symbolic data analysis for structural modification assessment. Eng. Struct. 2010; 32(3): 762–775. doi: 10.1016/j.engstruct.2009.12.004
   Web of Science ®Google Scholar
• Santos J, Crémona C, Orcesi A, Silveira P. Baseline-Free real-time novelty detection using vibration-based symbolic features. In EVACES’13, University of Ouro Preto, Ouro Preto, 2013: 1–8.
   Google Scholar
• Santos J, Calado L, Silveira P, Crémona C. Baseline-Based detection of structural changes: Drawbacks and an alternative approach. In EWSHM – European Workshop of Structural Health Monitoring, Bilbao, 2014: 1357–1364.
   Google Scholar
• Diday E, Noirhomme-Fraiture M. Symbolic Data Analysis and the SODAS Software. John Wiley and Sons: Chicester, 2008.
   Google Scholar
• Santos J, Orcesi AD, Crémona C, Silveira P. Baseline-Free real-time assessment of structural changes. Struct. Infrastruct. Eng. Maintenance, Manag. Life-Cycle Des. Perform. 2015;11(2):145–161. doi: 10.1080/15732479.2013.858169
   Web of Science ®Google Scholar
• Billard L, Diday E. Symbolic Data Analysis. vol. 52, no. 2. John Wiley and Sons: Chichester, 2006.
   Google Scholar
• Bishop CM. Pattern Recognition and Machine Learning. Springer: Berkeley, 2006.
   Google Scholar
• Theodoridis S, Koutroumbas K. Pattern Recognition. 4th edn. Elsevier: London, 2009.
   Google Scholar
• Hastie T. The Elements of Statistical Learning, Data Mining, Inference and Prediction. 2nd edn. Springer: New York, 2011.
   Google Scholar
• Kaufman L, Rousseeuw PJ. Finding Groups in Data. An Introduction to Cluster Analysis. 2nd ed. John Wiley & Sons, Inc: New York, NY, 1990.
   Google Scholar
• Kim Y, Chong JW, Chon KH, Kim J. Wavelet-Based AR–SVM for health monitoring of smart structures. Smart Mater. Struct. 2013; 22(1): 3–15.
   Web of Science ®Google Scholar
• Lautour OR, Omenzetter P. Damage classification and estimation in experimental structures using time series analysis and pattern recognition. Mech. Syst. Signal Process. 2010; 24(5): 1556–1569. doi: 10.1016/j.ymssp.2009.12.008
   Web of Science ®Google Scholar
• Krishnan Nair K, Kiremidjian AS. Time series based structural damage detection algorithm using Gaussian mixtures modeling. J. Dyn. Syst. Meas. Control. 2007; 129(3): 285–293. doi: 10.1115/1.2718241
   Web of Science ®Google Scholar
• Kesavan KN, Kiremidjian AS. A wavelet-based damage diagnosis algorithm using principal component analysis. Struct. Control Heal. Monit. 2012; 19(8): 672–685. doi: 10.1002/stc.462
   Web of Science ®Google Scholar