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Computational platform for probabilistic optimum monitoring planning for effective and efficient service life management

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Abstract

Over the past decades, significant advances have been accomplished in developing SHM techniques to detect the existing damages in deteriorating structures and maintenance techniques to extend the service life of these structures. The application of SHM can lead to more accurate damage detection. By using the information obtained from SHM, the uncertainties associated with structural performance assessment and prediction can be reduced. If the advanced SHM techniques are optimally integrated in life-cycle management, the efficiency and effectiveness of service life management of deteriorating structures can be maximized. In this paper, a computational platform for optimum monitoring planning based on multi-objective optimization (MOPT) and decision making is presented. The main components integrated in this computational platform are (a) formulation of objectives for optimum monitoring planning; (b) MOPT and decision making for application of the best monitoring plan; and (c) updating the damage propagation and structural performance prediction. The objectives for optimum monitoring planning are formulated considering the availability of monitoring data, damage detection, maintenance, service life and life-cycle cost. Through the MOPT and decision making, the best monitoring plan is determined. The updating process integrates the information obtained from monitoring to improve the accuracy and reduce the uncertainty associated with the damage occurrence and propagation prediction and monitoring planning.

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Acknowledgements

The support by grants from (a) the National Science Foundation (NSF) Award CMMI-1537926, (b) the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA), (c) the U.S. Federal Highway Administration (FHWA) Cooperative Agreement Award DTFH61-07-H-00040, (d) the U.S. Department of Transportation Region 3 University Transportation Center Grant CIAM-UTC-REG6, (e) the National Aeronautics and Space Administration (NASA) Award NNX10AJ20G and (f) the National Research Foundation of Korea (NRF) by Ministry of Science and ICT of Korean government Award NRF-2018R1C1B5044084 is gratefully acknowledged. The opinions presented in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk, 570-749, Republic of Korea

    Sunyong Kim

  2. Department of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh University, 117 ATLSS Dr, Bethlehem, PA, 18015-4729, USA

    Dan M. Frangopol

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  1. Sunyong Kim
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Correspondence to Dan M. Frangopol.

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Kim, S., Frangopol, D.M. Computational platform for probabilistic optimum monitoring planning for effective and efficient service life management. J Civil Struct Health Monit 10, 1–15 (2020). https://doi.org/10.1007/s13349-019-00365-4

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