TY - CONF A1 - Kapoor, M. A1 - Sørensen, J. D. A1 - Ghosh, S. A1 - Thöns, Sebastian T1 - Decision theoretic approach for identification of optimal proof load with sparse resistance information N2 - 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 analysis approach 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. T2 - 10th International Conference on Bridge Maintenance, Safety and Management (IABMAS) CY - Sapporo, Japan DA - 11.04.2021 KW - Proof load testing KW - Probabilistic decision analysis KW - Bridge resistance model PY - 2021 SN - 978-0-429-27911-9 U6 - https://doi.org/10.1201/9780429279119-104 SP - 789 EP - 797 PB - CRC Press CY - Boca Raton, Florida AN - OPUS4-57879 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kapoor, M. A1 - Overgaard Christensen, Ch. A1 - Wittrup Schmidt, J. A1 - Dalsgaard Sørensen, J. A1 - Thöns, Sebastian T1 - Decision analytic approach for the reclassification of concrete bridges by using elastic limit information from proof loading N2 - 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. KW - Proof loading KW - Structural reliability KW - Value of information KW - Decision analysis PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-596845 SN - 0951-8320/ VL - 232 SP - 1 EP - 16 PB - Elsevier Ltd. AN - OPUS4-59684 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -