Filtern
Erscheinungsjahr
Dokumenttyp
- Beitrag zu einem Tagungsband (21) (entfernen)
Schlagworte
- Structural health monitoring (5)
- Reliability (4)
- Inspection planning (3)
- Structural Health Monitoring (3)
- Bayesian updating (2)
- Environmental effects (2)
- Fatigue (2)
- Inspection (2)
- Risk (2)
- Structural systems (2)
Organisationseinheit der BAM
- 7 Bauwerkssicherheit (14)
- 7.2 Ingenieurbau (12)
- 7.0 Abteilungsleitung und andere (2)
- 7.1 Baustoffe (1)
- VP Vizepräsident (1)
- VP.1 eScience (1)
The fatigue deterioration modeling for welded steel structures subjected to high cycle fatigue is presented in the current document. The aim of this paper is to formulate approaches based on actual Research results for the phases fatigue crack initiation, fatigue propagation and fatigue failure. The physical characteristics of the approaches are discussed in combination with the associated uncertainties and the probabilistic modeling of this time variant reliability problem. The fatigue deterioration modeling documented here serve as a basis for a reliability-based approach to inspection planning.
A reliability-based approach to inspection planning for welded steel structures subjected to high cycle fatigue is presented in the current document. Inspections are an effective means to control the Progress of fatigue deterioration and the presented approach allows to determine the minimum required inspection effort so that the considered structure complies with the given risk acceptance criteria in terms of target reliability throughout its service life.
Inspections are an efficient means of enhancing the reliability of redundant structural Systems subjected to fatigue. To investigate the effect of such inspections, we represent the deterioration state of a Daniels System by means of a probabilistic fatigue crack growth model of all elements, which considers stochastic dependence among element fatigue behavior. We include inspection results in the calculation of the System collapse probability through Bayesian updating of the System deterioration state. Based on this approach, we calculate the collapse probability of a deteriorating Daniels System conditional on different inspection strategies in terms of inspection coverage and inspection times. The acceptability of an inspection strategy is verified by comparing the calculated collapse probabilities with maximum acceptable System failure probabilities. This study is a Step towards identifying optimal inspection strategies for redundant structural Systems subjected to fatigue.
A software prototype is developed for assessing and updating the reliability of single-cell prestressed concrete box girders subjected to chloride-induced reinforcement corrosion. The underlying system model consists of two integrated sub-models: a condition model for predicting the deterioration state of the box girder and a structural model for evaluating the overall system reliability. The condition model is based on a dynamic Bayesian network (DBN) model which considers the spatial variation of the corrosion process. Inspection data are included in the calculation of the system reliability through Bayesian updating on the basis of the DBN model. To demonstrate the effect of partial inspections, the software prototype is applied to a case study of a typical highway bridge with six spans. The case study illustrates that it is possible to infer the condition of uninspected parts of the structure due to the spatial correlation of the corrosion process.
This paper addresses the quantification of the value of structural health monitoring (SHM) before its implementation for structural systems on the basis of its Value of Information (VoI). The value of SHM is calculated utilizing the Bayesian pre-posterior decision analysis modelling the structural life cycle performance, the integrity management and the structural risks. The relevance and precision of SHM information for the reduction of the structural system risks and the expected cost of the structural integrity management throughout the life cycle constitutes the value of SHM and is quantified with this framework. The approach is focused on fatigue deteriorating structural steel systems for which a continuous resistance deterioration formulation is introduced. In a case study, the value of SHM for load monitoring is calculated for a Daniels system subjected to fatigue deterioration. The influence of and the value of SHM in regard to the structural system risks and the integrity management is explicated and explained. The results are pointing to the importance of the consideration of the structural system risks for the quantification of the value of SHM.
Growth of vehicle traffic density can be observed in many countries all over the world. This accretion is caused by world-wide population growth on the one hand, but also by increasing freight volumes and, thus, freight transports on the streets on the other hand.
This increased exposure becomes more and more of a problem for the civil infrastructure such as bridges. Many of these bridges are nowadays stressed to their limits by higher loads than they were originally designed for and/or operating times beyond the initially predicted life span. This raises questions about structural safety and lifetime prediction, of course, and therefore illustrates the need for accurate structural monitoring.
Since the lifetime of bridge structures is primarily influenced by their traffic loading, an accurate identification of load configurations over the whole length of a structure is most desirable.
In this paper, a method for vehicle load identification is proposed. It involves Bayesian Analysis and (quasi-)static importance functions in order to estimate vehicle positions, velocities and weights. The structure is modeled with finite elements in order to generate model predictions for different load configurations. The model predictions are compared to the actual measured data to identify the most probable loading configuration for that measurement. This involves the use of enhanced Monte Carlo simulations such as MCMC to reduce the computational effort. The measured data from different kinds of sensors can (and should) be combined for accuracy gain – in this case a combination of measured displacements and inclinations.
Since the measurements take place over some time during the passage of the vehicle, these estimations are carried out for several time instants for which the estimation is carried out. The advantage of using Bayesian Updating Method is the embodied learning effect leading to an improvement of the estimation when adding new information in a new calculation step.
Using the estimates for the loading conditions of a bridge structure as well as measurements of the structural responses, Bayesian analysis is again used in order to estimate localized structural parameters such as Young's modulus or Moments of Inertia in form of probability density functions yielding most probable values for the parameters.
Considering the difficulties for load identification close to the support poles of the bridge and therefore for the proposed structural parameter identification procedure, it is clear that this problem is ill posed. Bayesian regularization methods also have proven to be very effective when handling ill posed problems.
A novel method for risk-based optimization of inspection and repair strategies for deteriorating structural systems has recently been proposed. The method defines heuristics at the system level to reduce the number of possible strategies. For each defined strategy, it computes the updated system failure probability conditional on simulated inspection and repair histories, and evaluates the associated costs and risk. The expected total service life costs and risk for a strategy are finally determined using Monte Carlo simulation. The optimal strategy minimizes the expected total service life costs and risk. We intend to adopt this approach to optimize inspection, monitoring and repair activities for offshore wind park support structures. As a first step, we simulate – in analogy to an offshore wind park – the service life performance of an inspected group of jacket-type frames. The performance is quantified in terms of the group’s system failure probability conditional on simulated inspection and repair histories. The underlying system model accounts for the structural redundancy of the frames and the interdependence among their failure events due to similar loading conditions. The model also captures stochastic dependence among the deterioration states of the frames. As part of the simulation process the a-priori unknown outcome of any planned inspection is generated conditional on the outcome of all previous inspections.
Owners or operators of offshore wind farms perform inspections to collect information on the condition of the wind turbine support structures and perform repairs if required. These activities are costly and should be optimized. Risk-based methods can be applied to identify inspection and repair strategies that ensure an optimal balance between the expected total service life cost of inspection and repair, and the achieved risk reduction. Such an optimization requires explicit modeling of repairs. In this paper, the impact of different repair models on the results of a risk-based optimization of inspection and repair strategies is quantified in a numerical example considering a jacket-type steel frame subject to high-cycle fatigue. The example showed that, in this specific application, there is no need for detailed modeling of the behavior of repaired welded connections.
Identifying optimal inspection and repair strategies for offshore jacket structures is a challenging task. We pre-sent an approach, which is based on recent developments in the field of risk-based operation and maintenance planning at the structural system level. The approach utilizes heuristics to define inspection and repair strate-gies at the system level and to reduce the search space of possible strategies. For each defined strategy, the expected service life cost of inspection, repair and failure is evaluated based on simulated inspection and re-pair histories. Subset simulation is applied to compute the conditional repair and failure probabilities required for this analysis. It also forms the basis for simulating inspection and repair histories. The strategy that mini-mizes the expected service life cost is the optimal one in the set of pre-selected strategies. The underlying condition and performance model accounts for the stochastic dependence among the deterioration states of the different structural elements and the structural redundancy. The approach is demonstrated in a case study considering a jacket-type frame. In this study, we essentially vary the inspection interval, the minimum num-ber of inspected components and the target reliability, and identify the combination that minimizes the ex-pected total service life cost.
Inspection and maintenance contribute significantly to the lifetime cost of bridges. There is significant potential in using information obtained through structural health monitoring to update predictive models of the condition and performance of such structures, and thus ena-ble an improved decision-making regarding inspection and maintenance activities. Within the AISTEC project funded by the German Federal Ministry of Education and Research, we develop vibration-based structural health monitoring systems aimed at continuously provid-ing information on the structural condition of bridges. Environmental variations such as changing ambient temperatures can significantly influence the dynamic characteristics of bridges and thus mask the effect of structural changes and damages. It remains a challenge to account for such influences in structural health monitoring. To study the effect of ambient temperatures on the dynamic characteristics of beam structures, we monitor the vibration response of a reinforced concrete beam in the uncracked and cracked state at varying tem-peratures in a climate chamber. We postulate a set of competing parameterized probabilistic structural models, which explicitly account for the effect of varying ambient temperatures on the mechanical properties of the system. We then combine the information provided by the structural models with the information contained in the recorded vibration data to learn the parameters of the temperature-dependent structural models and infer the plausible state of the beam using Bayesian system identification and model class selection.