Decision support framework for bridge condition assessments

Björnsson, Ívar; Ivanov, Oskar Larsson; Honfí, Dániel; Leander, John

doi:10.1016/j.strusafe.2019.101874

Cited by 16 publications

(11 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alsharqawi et al [26] introduced the extended quality function deployment theory into bridge condition assessment based on visual inspection and ground penetrating radar data. Björnsson et al [27] considered to use the Bayesian decision theory for decisions of bridge condition assessment based on three domain knowledge, namely, modelling sophistication, consideration of uncertainty, and knowledge content. In addition, analytic hierarchy process [28] was also widely used in bridge condition assessment.…”

Section: B Bridge Condition Assessment Methodsmentioning

confidence: 99%

LR-BCA: Label Ranking for Bridge Condition Assessment

2021

View full text Add to dashboard Cite

Section: B Bridge Condition Assessment Methodsmentioning

confidence: 99%

LR-BCA: Label Ranking for Bridge Condition Assessment

2021

View full text Add to dashboard Cite

“…In [2] an optimal assessment method, as well as subsequent interventions, are evaluated using a Bayesian decision making framework. The assessment methods, which incorporated the possibility for collecting more structural health information, are then considered as possible experiments for obtaining new useful information about the condition of the structure.…”

Section: Fundamental Casementioning

confidence: 99%

“…In the condition assessment of common bridges, however, probabilistic assessment is rarely used. Therefore, a rational and systematic decision support framework is proposed in [2], with the aim of being applicable for practical cases, where deterministic approaches are used to verify structural performance. The framework enables to select the best assessment option and subsequent intervention if several alternatives with different costs and efficiency are available.…”

Section: Introductionmentioning

confidence: 99%

“…The current paper builds on [2] by investigating the implications of sequential decision making for maintenance decisions. In practice, the condition of a structure may be determined in a sequential process where the level of assessment and expected accuracy is increased at each progressive step.…”

Section: Introductionmentioning

confidence: 99%

“…This process may be repeated in certain instances where additional information is used to update model predictions leading to a sequential and reflective process. While the approach in [2] accounts for the suitability of adopting enhanced assessments in lieu of costly invasive actions, the current paper extend these considerations towards informed successive condition assessments and subsequent decision making. The term successive refers to the possibility of updating knowledge about the real condition more than once, e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Informed successive condition assessments in bridge maintenance

Honfí

Björnsson

Ivanov

et al. 2020

J Civil Struct Health Monit

Self Cite

View full text Add to dashboard Cite

The condition assessment of bridges considers a combination of information from different sources rendering multiple levels of assessment possible. This paper illustrates how successive condition assessment strategies increase the expected utility compared to single choice decisions through Bayesian inference. Multiple levels of assessment allow for additional possibilities for obtaining structural health information and updating one's beliefs about structural condition. Thus, more informed decision-making is possible with respect to the gain in accuracy versus the costs of the assessment options. The paper aims to introduce how the successive approach can be implemented and in which scenarios it provides an increase in expected utility in comparison to one instant decision. To highlight this, a few pedagogical numerical examples are provided.

show abstract

Anomaly detection using state‐space models and reinforcement learning

Khazaeli

Nguyen

Goulet

2021

Struct Control Health Monit

View full text Add to dashboard Cite

The early detection of anomalies associated with changes in the behavior of structures is important for ensuring their serviceability and safety. Identifying anomalies from monitoring data is prone to false and missed alarms due to the uncertain nature of the infrastructure responses' dependency on external factors such as temperature and loading. Existing anomaly detection strategies typically rely on univariate threshold values and disregard the planning horizon in the context of decision making. This paper proposes an anomaly detection framework that combines the interpretability of existing Bayesian dynamic linear models, a particular form of state-space models, with the longterm planning ability of reinforcement learning. The new framework provides (a) reinforcement learning formalism for anomaly detection in Bayesian dynamic linear models, (b) a method for simulating anomalies with respect to its height, duration, and time of occurrence, and (c) a method for quantifying anomaly detectability. The potential of the new framework is demonstrated on monitoring data collected on a bridge in Canada. The results show that the framework is able to detect real anomalies that were known to have occurred, as well as synthetic anomalies.

show abstract

Decision support framework for bridge condition assessments

Cited by 16 publications

References 22 publications

LR-BCA: Label Ranking for Bridge Condition Assessment

LR-BCA: Label Ranking for Bridge Condition Assessment

Informed successive condition assessments in bridge maintenance

Anomaly detection using state‐space models and reinforcement learning

Contact Info

Product

Resources

About