RT
Rui Teixeira
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4 records found
1
Transport network resilience
A mapping and sensitivity analysis strategy to improve the decision-making process during extreme weather events
This paper aims to move forward in the understanding of resilience by improving the quality of available information during the decision-making process. A dynamic methodology together with a bounded travel time cost function is used to quantify the transport resilience, and an analysis of the main parameters is performed by a mapping of possible scenarios and a sensitivity analysis. Due to the complexity of the model, a recent methodology for the sensitivity analysis is presented. This approach is a bi-phase sensitivity analysis with a combination of a local and a global method. This allows an early detection of the parameters that will have a larger impact on the network performance when the hazard occurs, and together with the mapping strategy, make possible to select the best ways to increase the resilience of the transport network at any stage of the process, i.e., before during and after the damaging event.
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This paper aims to move forward in the understanding of resilience by improving the quality of available information during the decision-making process. A dynamic methodology together with a bounded travel time cost function is used to quantify the transport resilience, and an analysis of the main parameters is performed by a mapping of possible scenarios and a sensitivity analysis. Due to the complexity of the model, a recent methodology for the sensitivity analysis is presented. This approach is a bi-phase sensitivity analysis with a combination of a local and a global method. This allows an early detection of the parameters that will have a larger impact on the network performance when the hazard occurs, and together with the mapping strategy, make possible to select the best ways to increase the resilience of the transport network at any stage of the process, i.e., before during and after the damaging event.
The present paper discusses the application principles of value of information theory in adaptive metamodeling for reliability analysis. Metamodeling for reliability purposes has become particularly relevant in recent years. The usage of metamodels allows surrogating the, costly to evaluate, performance functions of engineering structures. Adaptive Kriging procedures are examples of the successful application of metamodel- ing in reliability analysis. Efficient adaptive Kriging involves the usage of some notion of improvement in what ultimately is an unsupervised decision making scheme that selects points to enrich the model. Therefore, the decision to select a point to enrich the experimental design should consider the utility of each candidate in the expectation of improvement of the metamodeling accuracy. Within this context, a comprehensive discussion on the application of value of information for reliability metamodeling is presented. Since the candidate points and surrogate are jointly built in a virtually costless model, it is possible to know the virtual outcome of the enrich- ment decisions. In many circumstances, points in the experimental design may provide redundant information. Furthermore, a priori knowledge on the performance function may be applied to weight the expected outcome of exploration and exploitation. Value of information considerations adds value to reliability metamodeling that uses adaptive methods, and is of interest for efficient design and optimization of complex structures, such as bridge structures.
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The present paper discusses the application principles of value of information theory in adaptive metamodeling for reliability analysis. Metamodeling for reliability purposes has become particularly relevant in recent years. The usage of metamodels allows surrogating the, costly to evaluate, performance functions of engineering structures. Adaptive Kriging procedures are examples of the successful application of metamodel- ing in reliability analysis. Efficient adaptive Kriging involves the usage of some notion of improvement in what ultimately is an unsupervised decision making scheme that selects points to enrich the model. Therefore, the decision to select a point to enrich the experimental design should consider the utility of each candidate in the expectation of improvement of the metamodeling accuracy. Within this context, a comprehensive discussion on the application of value of information for reliability metamodeling is presented. Since the candidate points and surrogate are jointly built in a virtually costless model, it is possible to know the virtual outcome of the enrich- ment decisions. In many circumstances, points in the experimental design may provide redundant information. Furthermore, a priori knowledge on the performance function may be applied to weight the expected outcome of exploration and exploitation. Value of information considerations adds value to reliability metamodeling that uses adaptive methods, and is of interest for efficient design and optimization of complex structures, such as bridge structures.
Reliability assessment with adaptive Kriging has gained notoriety due to the Kriging capability of accurately replacing the performance function while performing as a self-improving function for learning procedures. Recent works on adaptive Kriging pursued to improve the efficiency of the active learning through the application of distinct learning functions, sampling methods, or frameworks to assess the learning space. Within this context, the present work exploits three innovative applications of density scanning to improve the efficiency of the adaptive Kriging. Density scanning has significant synergies with adaptive Kriging implementation. For most learning criteria, candidate points occur in dense clusters. This is due to the fact that the most efficient learning strategies pursue to improve predictions near the failure region, or when the prediction uncertainty is large. Identifying dense clusters of points, and fomenting exploitation of these, parallelizing computations, and limiting the generation of dense clusters in the design of experiments are examples of learning frameworks that can be achieved with density scanning. Three reference examples are researched in the present work, a complex function, a series system, and a relatively high dimension engineering problem. For all the cases, the application of density scanning is identified to improve the active learning efficiency.
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Reliability assessment with adaptive Kriging has gained notoriety due to the Kriging capability of accurately replacing the performance function while performing as a self-improving function for learning procedures. Recent works on adaptive Kriging pursued to improve the efficiency of the active learning through the application of distinct learning functions, sampling methods, or frameworks to assess the learning space. Within this context, the present work exploits three innovative applications of density scanning to improve the efficiency of the adaptive Kriging. Density scanning has significant synergies with adaptive Kriging implementation. For most learning criteria, candidate points occur in dense clusters. This is due to the fact that the most efficient learning strategies pursue to improve predictions near the failure region, or when the prediction uncertainty is large. Identifying dense clusters of points, and fomenting exploitation of these, parallelizing computations, and limiting the generation of dense clusters in the design of experiments are examples of learning frameworks that can be achieved with density scanning. Three reference examples are researched in the present work, a complex function, a series system, and a relatively high dimension engineering problem. For all the cases, the application of density scanning is identified to improve the active learning efficiency.