Efficient Structural System Reliability Updating with Subspace-Based Damage Detection Information

International audienceDamage detection systems and algorithms (DDS and DDA) provide information of the structural system integrity in contrast to e.g. local information by inspections or non-destructive testing techniques. However, the potential of utilizing DDS information for the structural integrity assessment and prognosis is hardly exploited nor treated in scientific literature up to now. In order to utilize the information provided by DDS for the structural performance, usually high computational efforts for the pre-determination of DDS reliability are required. In this paper, an approach for the DDS performance modelling is introduced building upon the non-destructive testing reliability which applies to structural systems and DDS containing a strategy to overcome the high computational efforts for the pre-determination of the DDS reliability. This approach takes basis in the subspace-based damage detection method and builds upon mathematical properties of the damage detection algorithm. Computational efficiency is gained by calculating the probability of damage indication directly without necessitating a pre-determination for all damage states. The developed approach is applied to a static, dynamic, deterioration and reliability structural system model, demonstrating the potentials for utilizing DDS for risk reduction

Determination of structural and damage detection system influencing parameters on the value of information

International audienceA method to determine the influencing parameters of a structural and Damage Detection System (DDS) is proposed based on the Value of Information (VoI) analysis. The VoI analysis utilizes the Bayesian pre-posterior decision theory to quantify the value of DDS for the structural integrity management during service life. First the influencing parameters of the structural system, such as deterioration type and rate are introduced for the performance of the prior probabilistic system model. Then the influencing parameters on the DDS performance, including number of sensors, sensor locations, measurement noise and the Type I error are investigated. The pre-posterior probabilistic model is computed utilizing the Bayes' theorem to update the prior system model with the damage indication information. Finally, the value of DDS is quantified as the difference between the maximum utility obtained in pre-posterior and prior analysis based on the decision tree analysis, comprising structural probabilistic models, consequences, as well as benefit and costs analysis associated with and without monitoring. With the developed approach, a case study on a statically determinate Pratt truss bridge girder is carried out to validate the method. The analysis shows that the deterioration rate is the most sensitive parameter on the effect of relative VoI over the whole service life. Furthermore, it shows that more sensors do not necessarily lead to a higher relative VoI; only specific sensor locations near the highest utilized components lead to a high relative VoI; measurement noise and the Type I error should be controlled and be as small as possible. An optimal sensor employment with highest relative VoI is found. Moreover, it is found that the proposed method can be a powerful tool to develop optimal service life maintenance strategies-before implementation-for similar bridges and to optimize the DDS settings and sensor configuration for minimum expected costs and risks

On damage detection system information for structural systems

International audienceDamage 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

Contextualize Me -- The Case for Context in Reinforcement Learning

While Reinforcement Learning ( RL) has made great strides towards solving increasingly complicated problems, many algorithms are still brittle to even slight environmental changes. Contextual Reinforcement Learning (cRL) provides a framework to model such changes in a principled manner, thereby enabling flexible, precise and interpretable task specification and generation. Our goal is to show how the framework of cRL contributes to improving zero-shot generalization in RL through meaningful benchmarks and structured reasoning about generalization tasks. We confirm the insight that optimal behavior in cRL requires context information, as in other related areas of partial observability. To empirically validate this in the cRL framework, we provide various context-extended versions of common RL environments. They are part of the first benchmark library, CARL, designed for generalization based on cRL extensions of popular benchmarks, which we propose as a testbed to further study general agents. We show that in the contextual setting, even simple RL environments become challenging - and that naive solutions are not enough to generalize across complex context spaces.Comment: arXiv admin note: substantial text overlap with arXiv:2110.0210

Фінансове забезпечення інноваційного розвитку фармбізнесу: глобальні виклики

Глобальні загрози життєдіяльності населення безперешкодно охоплюють держави як с потужною економікою так і ті, що розвиваються. В умовах коли Уряд не в змозі сприяти створенню власного потужного та ефективного фармацевтичного бізнесу. Слід сприяти забезпеченню національного ринку імпортними інноваційними препаратами та втілювати західну модель економіки здоров’я, як передумови інноваційного гармонійного розвитку фармбізнесу