Structural health monitoring needs to produce actionable information regarding structural integrity that supports operational and maintenance decision making that is individualized for a given structure and its performance objectives. An effective Prognostics and Health Management (PHM) framework for aging structures (subjected to physical, chemical, environmental, and mechanical degradation) needs to integrate four elements – damage modeling, monitoring, data analytics, and uncertainty quantification. This paper briefly discusses available techniques and ongoing challenges in each of these four elements of PHM, in the context of concrete structures. A Bayesian network approach is discussed for integrating heterogeneous information from multi-physics computational models of degradation processes, full-field measurement techniques, big data analytics, and various data and model uncertainty sources. Such a comprehensive framework can quantitatively support decisions regarding appropriate risk management actions.
How to Cite
concrete structures, damage modeling, uncertainty quantification, diagnosis, prognosis, Bayesian networks
Mehta, P .K. and Monteiro, P . (2001). Concrete — Microstructure, Properties and Materials, Prentice-Hall, Englewood Cliffs, New Jersey.
Chen, D.(2006), “Computational Framework for Durability Assessment of Reinforced Concrete Structures under Coupled Deterioration Processes,” Ph.D. Dissertation, Vanderbilt University, Nashville, TN.
Thoft-Christensen, P. (2003) Corrosion and Cracking of Reinforced Concrete, in Life-Cycle Performance of Deteriorating Structures: Assessment, Design and Management, ASCE, pp. 26-36.
Saetta, A.V. and Vitaliani, R.V. (2004) Experimental investigation and numerical modeling of carbonation process in reinforced concrete structures, Cement and Concrete Research, Vol. 34, No. 4, pp. 571-579.
Chen, D., and Mahadevan, S. (2008), “Chloride-Induced Reinforcement Corrosion and Concrete Cracking Simulation,” Cement and Concrete Composites, Vol. 30, pp. 227-238, No. 3.
Sarkar, S., Mahadevan, S., J.C.L. Meeussen, H. van der Sloot, D.S. Kosson (2010), “Numerical Simulation of Cementitious Materials Degradation Under External Sulfate Attack,” Cement and Concrete Composites, Vol. 32, No. 3, pp. 241-252.
Sarkar, S., Kosson, D.S., Mahadevan, S., Meeussen, J.C.L., van der Sloot, H., Arnold, J.R., Brown, K.G (2012).“Bayesian calibration of thermodynamic parameters for geochemical speciation modeling of cementitious materials,” Cement and Concrete Research, Vol. 42, No. 7, pp. 889-902.
Clayton, D. (2014), “Nondestructive Evaluation Techniques for Nuclear Power Plant Concrete Structures,” Light Water Reactor Sustainability (LWRS) Newsletter, Issue 14, U. S. Department of Energy.
Mares, J., Miller, J., Rhoads, J., Son, S., Groven, L., Sharp, N., and Adams, D. (2013), “Thermal and Mechanical Response of PBX 9501, PBS 9501, and 900-21 under High-Frequency Mechanical Excitation,”, Journal of Applied Physics, 113, 084904.
Sharp, N., P. O’Regan, Adams, D. E., Caruthers, J., David, A., and Suchomel, M. (2014), “Lithium-Ion Battery Electrode Inspection using Pulse Thermography,”NDT&E International, Vol. 64, pp. 41- 51.
Bond, R., Underwood, S., Adams, D., and Cummins, J. (2013), “Structural Health Monitoring-Based Methodologies for Managing Uncertainty in Aircraft Structural Life Assessment,” Proceedings of the 9th International Workshop on Structural Health Monitoring, Palo Alto, CA.
Prajapati, V.(2013), Big Data Analytics with R and Hadoop, Packt Publishing, Birmingham, UK.
Sankararaman, S., and Mahadevan, S. (2011), “ Likelihood- Based Representation of Epistemic Uncertainty due to Sparse Point Data and Interval Data,” Reliability Engineering and System Safety. Vol. 96, No. 7, pp. 814-824.
Sankararaman, S., and Mahadevan, S. (2013), “Separating the Contributions of V ariability and Parameter Uncertainty in Probability Distributions,” Reliability Engineering & System Safety, Vol. 112, pp. 187-199.
Liang, B., and Mahadevan, S. (2011), “Error and Uncertainty Quantification and Sensitivity Analysis of Mechanics Computational Models,” International Journal for Uncertainty Quantification. Vol. 1, No. 2, pp. 147-161.
Jensen, F. V., An Introduction to Bayesian Networks. Springer-Verlag, 1996.
Bartram, G., and Mahadevan, S. (2014), “Integration of Heterogeneous Information in SHM Models,” Structural Control and Health Monitoring, Vol. 21, No. 3, pp. 403-422.
Sankararaman, S., Ling, Y., Shantz, C. and Mahadevan, S. (2011), “Uncertainty Quantification in Fatigue Crack Growth Prognosis,” International Journal of Prognostics and Health Management, Vol. 2, no. 1.
Sankararaman, S., and Mahadevan, S.(2013), “Bayesian methodology for diagnosis uncertainty quantification and health monitoring,” Structural Control and Health Monitoring, Vol. 20, pp. 88-106.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.