A Knowledge Management Platform for Infrastructure Performance Modeling
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A Knowledge Management Platform for Infrastructure Performance Modeling

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  • Abstract:
    Performance models are used to process condition data in order to assess current needs, and to forecast (the effect of interventions/investments on) future condition, and in turn (on) the remaining service-life or time-to-failure distribution of infrastructure facilities and their components. Performance models are mathematical expressions that relate condition data to a set of explanatory variables such as design characteristics, traffic loading, environmental factors, and history of maintenance activities. The motivation for this work is that in the last 40 years, and particularly in the last 10-15 years with developments in statistics and in computer availability, storage capacity and power, numerous techniques have been employed to estimate these models under different structural assumptions, using countless data types and sources, and to address and support a plethora of managerial decisions, i.e., design, construction, maintenance and rehabilitation. Unfortunately, these efforts have been disconnected and as a result there is a lack of standards/criteria for the development, utilization, evaluation and selection of performance models. Thus, the objective of the study is to build an online platform/repository/test-bed to facilitate the exchange of data and information related to performance models as a step to correct this significant problem. Specifically, the platform would contribute to the science and practice of performance modeling by: (1) Serving as a central, reliable source of relevant data and information; and (2) Providing on-line access to a test-bed that would allow analysts to evaluate the capabilities of their own models. The platform advances infrastructure performance modeling because analysts are able to build and benefit from existing knowledge (as opposed to starting from scratch). Moreover, analysts would be able to assess and compare their assumptions and models, i.e., the nature of their contributions, against well-established, state-of-the-art benchmarks.
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