TY - JOUR
A1 - Thöns, Sebastian
A1 - Döhler, M.
A1 - Long, Lijia
T1 - On Damage Detection System Information for Structural Systems
N2 - Damage detection systems (DDS) provide information of the structural system integrity in contrast to e.g. local information by inspections or non-destructive testing techniques. In this paper, an approach is developed and demonstrated to utilize DDS information to update the structural system reliability and to integrate this information in structural system risk and utility analyses. For this aim, a novel performance modelling of DDS building upon their system characteristics and non-destructive testing reliability is introduced. The DDS performance modelling accounts for a measurement system in combination with a damage detection algorithm attached to a structural system in the reference and damage states and is modelled with the probability of indication accounting for type I and II errors. In this way, the basis for DDS performance comparison and assessment is provided accounting for the dependencies between the damage states in a structure. For updating of the structural system reliability, an approach is developed based on Bayesian updating facilitating the use of DDS information on structural system level and thus for a structural system risk analysis. The structural system risk analysis encompasses the static, dynamic, deterioration, reliability and consequence models, which provide the basis for the system model for calculating the direct risks due to component failure and the indirect risks due to system failure. Two case studies with the developed approach demonstrate a high Value of DDS Information due to risk and expected cost reduction.
KW - Damage detection
KW - Value of Information
KW - Structural systems
KW - Damage detection uncertainty
PY - 2018
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000451383100004
U6 - http://dx.doi.org/10.1080/10168664.2018.1459222
VL - 28
IS - 3
SP - 255
EP - 268
PB - Taylor & Francis
AN - OPUS4-46964
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
A1 - Thöns, Sebastian
T1 - On the Value of Monitoring Information for the Structural Integrity and Risk Management
N2 - This article introduces an approach and framework for the quantification of the value of structural health monitoring (SHM) in the context of the structural risk and integrity management for systems. The quantification of the value of SHM builds upon the Bayesian decision and utility theory, which facilitates the assessment of the value of information associated with SHM. The principal approach for the quantification of the value of SHM is formulated by modeling the fundamental decision of performing SHM or not in conjunction with their expected utilities. The expected utilities are calculated accounting for the probabilistic performance of a system in conjunction with the associated structural integrity and risk management actions throughout the life cycle, the associated benefits, structural risks, and costs and when performing SHM, the SHM information, their probabilistic outcomes, and costs. The calculation of the expected utilities necessitates a comprehensive and rigorous modeling, which is introduced close to the original formulations and for which analysis characteristics and simplifications are described and derived. The framework provides the basis for the optimization of the structural risk and integrity management based on utility gains including or excluding SHM and inspection information. Studies of fatigue deteriorating structural Systems and their characteristics (1) provide decision Support for the performance of SHM, (2) explicate the influence of the structural component and system characteristics on the value of SHM, and (3) demonstrate how an integral optimization of SHM and inspection strategies for an efficient structural risk and integrity management can be performed.
KW - Monitoring
KW - Structural Integrity and Risk Management
KW - SHM
PY - 2018
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS&DestLinkType=FullRecord;UT=WOS:000423078100007
U6 - http://dx.doi.org/10.1111/mice.12332
VL - 33
IS - 1
SP - 79
EP - 94
PB - John Wiley & Sons, Inc.
CY - Hoboken, NJ, USA
AN - OPUS4-44537
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Long, Lijia
A1 - Thöns, Sebastian
A1 - Döhler, M.
T1 - The effects of SHM system parameters on the value of damage detection information
N2 - This paper addresses how the value of damage detection Information depends on key Parameters of the Structural Health Monitoring (SHM) system including number of sensors and sensor locations. The Damage Detection System (DDS) provides the information by comparing ambient vibration measurements of a (healthy) reference state with measurements of the current structural system. The performance of DDS method depends on the physical measurement properties such as the number of sensors, sensor positions, measuring length and sensor type, measurement noise, ambient excitation and sampling frequency, as well as on the data processing algorithm including the chosen type I error for the indication threshold. The quantification of the value of Information (VoI) is an expected utility based Bayesian decision analysis method for quantifying the difference of the expected economic benefits with and without information. The (pre-)posterior probability is computed utilizing the Bayesian updating theorem for all possible indications. If changing any key parameters of DDS, the updated probability of system failure given damage detection information will be varied due to different indication of probability of damage, which will result in changes of value of damage detection information. The DDS system is applied in a statically determinate Pratt truss bridge girder. Through the analysis of the value of information with different SHM system characteristics, the settings of DDS can be optimized for minimum expected costs and risks before implementation.
T2 - 9th European Workshop on Structural Health Monitoring
CY - Manchester, UK
DA - 10.07.2018
KW - SHM
KW - Damage detection system
KW - Value of information
PY - 2018
SP - 375
EP - 384
AN - OPUS4-46190
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Berchtold, Florian
A1 - Knaust, Christian
A1 - Rogge, Andreas
A1 - Arnold, L.
A1 - Thöns, Sebastian
ED - Lönnermark, Anders
ED - Ingason, Haukur
T1 - Risk Analysis for Road Tunnels – A Metamodel to Efficiently Integrate Complex Fire Scenarios
N2 - Fires in road tunnels constitute complex scenarios with interactions between the fire, tunnel users and safety measures. More and more methodologies for risk analysis quantify the consequences of these scenarios with complex models. Examples for complex models are the computational fluid dynamics model Fire Dynamics Simulator (FDS) and the microscopic evacuation model FDS+Evac. However, the high computational effort of complex models often limits the number of scenarios in practice. To balance this drawback, the scenarios are often simplified. Accordingly, there is a challenge to consider complex scenarios in risk analysis.
To face this challenge, we improved the metamodel used in the methodology for risk analysis presented on ISTSS 2016. In general, a metamodel quickly interpolates the consequences of few scenarios simulated with the complex models to a large number of arbitrary scenarios used in risk analysis. Now, our metamodel consists of the projection array-based design, the moving least squares method, and the prediction interval to quantify the metamodel uncertainty. Additionally, we adapted the projection array-based design in two ways: the focus of the sequential refinement on regions with high metamodel uncertainties; and the combination of two experimental designs for FDS and FDS+Evac.
To scrutinise the metamodel, we analysed the effects of three sequential refinement steps on the metamodel itself and on the results of risk analysis. We observed convergence in both after the second step (ten scenarios in FDS, 192 scenarios in FDS+Evac). In comparison to ISTSS 2016, we then ran 20 scenarios in FDS and 800 scenarios in FDS+Evac. Thus, we reduced the number of scenarios remarkably with the improved metamodel. In conclusion, we can now efficiently integrate complex scenarios in risk analysis. We further emphasise that the metamodel is broadly applicable on various experimental or modelling issues in fire safety engineering.
T2 - International Symposium on Tunnel Safety and Security
CY - Boras, Sweden
DA - 14.03.2018
KW - Risk
KW - Metamodel
KW - CFD
KW - Evacuation
KW - Uncertainty
PY - 2018
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-445354
SN - 978-91-88695-48-2
VL - 8
SP - 349
EP - 360
AN - OPUS4-44535
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -