TY - GEN
T1 - Stochastic differential equations for modeling deterioration of engineering systems and calibration based on structural health monitoring data
AU - Iannacone, L.
AU - Gardoni, P.
N1 - Funding Information:
This work was supported by the National Institute of Standards and Technology (NIST) through the Center for Risk-Based Community Resilience Planning under Award No 70NANB15H044. Opinions and findings presented are those of the writers and do not necessarily reflect the views of the sponsor.
Publisher Copyright:
© 2019 Taylor & Francis Group, London
PY - 2019
Y1 - 2019
N2 - Different formulations have been proposed to estimate the reliability of engineering systems according to their design specification. However, the as-built state might differ from the design specification, and aging and deterioration are affecting the performance of the systems over time. Physics-based fragility estimates that are explicitly developed as functions of state variables defining the state of the system can be coupled with state-dependent models for the deterioration and results from Structural Health Monitoring (SHM). State-of-the-art state-dependent models for the deterioration of the state variables are able to incorporate the possible interaction between different deterioration processes and can be used as inputs in the physics-based fragilities to estimate the future reliability of the system. SHM can be used to assess the current state of the system as well as to calibrate the state-dependent deterioration models, which can then be used to better predict the performance of the system at future times. Current state-dependent models are defined in terms of the finite change in the state variables in the unit of time of a chosen discretization for the time domain. This makes the model parameters discretization-dependent and makes the simulation and calibration processes time-consuming and computationally expensive. In this paper, we propose a novel formulation that uses a system of Stochastic Differential Equations (SDE) for modeling the change in the state variables of the system. Using this formulation, the model parameters do not depend on the chosen discretization in the time domain. In addition, it is possible to use the results from stochastic calculus and the properties of diffusion processes to speed up the simulation and calibration of the models
AB - Different formulations have been proposed to estimate the reliability of engineering systems according to their design specification. However, the as-built state might differ from the design specification, and aging and deterioration are affecting the performance of the systems over time. Physics-based fragility estimates that are explicitly developed as functions of state variables defining the state of the system can be coupled with state-dependent models for the deterioration and results from Structural Health Monitoring (SHM). State-of-the-art state-dependent models for the deterioration of the state variables are able to incorporate the possible interaction between different deterioration processes and can be used as inputs in the physics-based fragilities to estimate the future reliability of the system. SHM can be used to assess the current state of the system as well as to calibrate the state-dependent deterioration models, which can then be used to better predict the performance of the system at future times. Current state-dependent models are defined in terms of the finite change in the state variables in the unit of time of a chosen discretization for the time domain. This makes the model parameters discretization-dependent and makes the simulation and calibration processes time-consuming and computationally expensive. In this paper, we propose a novel formulation that uses a system of Stochastic Differential Equations (SDE) for modeling the change in the state variables of the system. Using this formulation, the model parameters do not depend on the chosen discretization in the time domain. In addition, it is possible to use the results from stochastic calculus and the properties of diffusion processes to speed up the simulation and calibration of the models
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M3 - Conference contribution
AN - SCOPUS:85063930230
SN - 9781138626331
T3 - Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018
SP - 1663
EP - 1670
BT - Life-Cycle Analysis and Assessment in Civil Engineering
A2 - Frangopol, Dan M.
A2 - Caspeele, Robby
A2 - Taerwe, Luc
PB - CRC Press/Balkema
T2 - 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018
Y2 - 28 October 2018 through 31 October 2018
ER -