Value of information and value of decisions

This paper introduces an extended formulation of decision analyses, which provides an enhanced basis for the definition of the value of information, the value of actions and the value of actions and information analyses. The formulation of decision analyses is (1) extended by introducing an action implementation uncertainty and - following the reasoning of Raiffa and Schlaifer (1961) - (2) by considering both the information acquirement state and the action implementation state jointly and separately for the definition of the types of decision analyses. Decision value analyses are derived by explicitly distinguishing and addressing the cause of the expected utility gain namely by information, by actions or by both action and information. It is shown how different optimal sets of information and actions and their acquirement or implementations states, respectively, lead to different decision value classifications. Published studies and applied decision and decision value analyses are analyzed showing a diversity beyond the original definitions by Raiffa and Schlaifer (1961), however, also being less diverse in comparison to the introduced classification in this paper

Value of information and value of decisions

This paper introduces an extended formulation of decision analyses, which provides an enhanced basis for the definition of the value of information, the value of actions and the value of actions and information analyses. The formulation of decision analyses is (1) extended by introducing an action implementation uncertainty and - following the reasoning of Raiffa and Schlaifer (1961) - (2) by considering both the information acquirement state and the action implementation state jointly and separately for the definition of the types of decision analyses. Decision value analyses are derived by explicitly distinguishing and addressing the cause of the expected utility gain namely by information, by actions or by both action and information. It is shown how different optimal sets of information and actions and their acquirement or implementations states, respectively, lead to different decision value classifications. Published studies and applied decision and decision value analyses are analyzed showing a diversity beyond the original definitions by Raiffa and Schlaifer (1961), however, also being less diverse in comparison to the introduced classification in this paper

A decision theoretic approach towards planning of proof load tests

Accurate determination of the bearing capacity of bridges is of high importance for society. Concerns are raised about the actual bearing capacity of bridges due to aging related deterioration, ever increasing traffic loads and conservative design. Proof load testing is often used for evaluations of bridge capacity. However, extensive proof load tests tend to be costly. Further, the risks of damage to the bridge imply that a proof load test may not always be cost effective. The performance of proof load testing and its outcomes is further dependent on factors such as the chosen loading, the monitoring technology and methods, and the stop criteria. A decision theoretic approach is utilized to demonstrate the optimal strategy for proof load testing procedures and collection of information. The decision scenario constituting the planning and performance of the proof loading is considered along with prevention of damage to the bridge. The decision maker is the proof loading planner who chooses the loading, the monitoring technologies and methods as well as the stop criteria to minimize the expected costs of the test and to comply with acceptable risks. A case study is developed and the optimal strategy with respect to loading, monitoring technology and stop criteria is identified as those with the maximum utility to the decision maker.The financial support and assistance from the Danish Road Directorate is greatly acknowledged

Challenges Related to Probabilistic Decision Analysis for Bridge Testing and Reclassification

This paper reviews historical developments and recent challenges in full scale bridge testing and introduces results- and hypotheses related to an ongoing bridge testing research project. This research project encompasses full scale bridge testing in conjunction with bearing capacity analysis as well as related contact- and non-contact monitoring procedures combined with a decision analytical approach. Results from the first steps of the project, focusing on full scale load testing of bridges, are presented. The next part approaches the interfaces between three project areas namely the bearing capacity analysis, the utilization of monitoring procedures and a decision analytical approach. The proposed probabilistic decision analysis approach is described for two scenarios: (1) The decision support for the actual proof load test providing decision rules for a safe and efficient in-situ test and (2) for the identification of efficient strategies for the bridge reclassification accounting for modeling, simulation, and monitoring information. The paper concludes with a summary highlighting deemed challenges in the used approaches

Decision theoretic approach for identification of optimal proof load with sparse resistance information

Proof load testing may be performed to confirm the reliability of the bridge for an existing classification or to prove the reliability for a higher classification.In this paper, a probabilistic decision analysisapproach is applied to the scenario for the evaluation of target proof load in the situation where information on the bridge resistance model is lacking. In this case, the resistance model is established by proof loading and taking very basic prior knowledge into account. The decision scenario is modelled in the context of the proof load test planner who shall choose the required load level for assessment of a bridge. The choice of the load level depends on the risks due to the testing and the expected benefit gain from the test. Information acquired about the loading response from monitoring during the proof load testing is modelled by taking basis in the model uncertainty formulation. The optimal proof load level for classification of a single lane, simply supported bridge of 8m span subjected to live load from very heavy (gross weight > 80 tons) transport vehicles was calculated. The optimal proof load level was identified as leading to a positive expected benefit gain to the decision maker while also satisfying target reliability criteria for remaining service life. The analysis was performed for the evaluation of bridge performance with respect to five classifications of very heavy transport vehicles with different vehicle weights and configurations

Decision analytic approach for the reclassification of concrete bridges by using elastic limit information from proof loading

Reclassification of bridges, i.e., a change in load rating, using reliability-based methods and a direct update with proof load information has been presented by many authors. However, bridge reclassification has hardly been studied from a decision analytic perspective, i.e., with quantification of the risks and benefits of different classification choices, and the expected benefit gain from proof loading. We derive, explain and exemplify a decision analytic approach for bridge reclassification along with models for (1) elastic and ultimate capacity and their adaptation with proof load information, (2) proof load information with classification outcomes accounting for target reliabilities and, (3) utilities including socio-economic benefits from reclassification. The approach and models are exemplified with a case study based on reclassification of bridges with a low existing classification. Decision rules, for practical use by a highway authority to find the optimal classification, are identified and documented based on: (1) the measurement of the capacity at elastic limit by proof loading, (2) the bridge reclassification benefits, and, (3) the required annual reliability level. From a Value of Information analysis, it is concluded that the proof load information is highly valuable for reclassification in cases of high socio-economic benefits and high reliability requirements