Recently, several wireless sensor network studies demonstrated large discrepancies between experimentally observed communication properties and properties produced by widely used simulation models. Our first goal is to provide sound foundations for conclusions drawn from these studies by extracting relationships between location (e.g distance) and communication properties (e.g. reception rate) using non-parametric statistical techniques. The objective is to provide a probability density function that completely characterizes the relationship. Furthermore, we study individual link properties and their correlation with respect to common transmitters, receivers and geometrical location.The second objective is to develop a series of wireless network models that produce networks of arbitrary sizes with realistic properties. We use an iterative improvement-based optimization procedure to generate network instances that are statistically similar to empirically observed networks. We evaluate the accuracy of our conclusions using our models on a set of standard communication tasks, like connectivity maintenance and routing.

SUMMARYWhile structural engineers have traditionally focused on individual components (bridges, for example) of transportation networks for design, retrofit, and analysis, it has become increasingly apparent that the economic costs to society after extreme earthquake events are caused at least as much from indirect costs as direct costs due to individual structures. This paper describes an improved methodology for developing probabilistic estimates of repair costs and repair times that can be used for evaluating the performance of new bridge design options and existing bridges in preparation for the next major earthquake. The proposed approach in this paper is an improvement on previous bridge loss modeling studies-it is based on the local linearization of the dependence between repair quantities and damage states so that the resulting model follows a linear relationship between damage states and repair points. The methodology uses the concept of performance groups (PGs) that account for damage and repair of individual bridge components and subassemblies. The method is validated using two simple examples that compare the proposed method to simulation and previous methods based on loss models using a power-law relationship between repair quantities and damage. In addition, an illustration of the method is provided for a complete study on the performance of a common five-span overpass bridge structure in California. Intensity-dependent repair cost ratios (RCRs) and repair times are calculated using the proposed approach, as well as plots that show the disaggregation of repair cost by repair quantity and by PG. This provides the decision maker with a higher fidelity of data when evaluating the contribution of different bridge components to the performance of the bridge system, where performance is evaluated in terms of repair costs and repair times rather than traditional engineering quantities such as displacements and stresses.

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In this study probabilistic seismic loss models for reinforced concrete bridges are improved with separate models for connecting damage to repair quantities and repair quantities to money and time costs. This approach allows explicit consideration of repair design and variability of cost and time estimating that are not captured using a direct relationship between damage and loss. The proposed repair and cost models require schematic designs of bridge repairs and cost estimations to determine the model parameters, which were completed for three scenarios of damage on a single bridge. These models were used to analyze the probabilistic losses on different bridge types, specifically repair costs and repair effort. These analyses can be utilized for direct and indirect seismic loss assessments for transportation networks. All data in the proposed models were parameterized in terms of bridge properties, allowing extrapolation to bridges within the same class. The procedure is also portable for different bridge classes and state practices.

Emerging wireless sensor technology presents a tremendous opportunity for developing low-cost monitoring systems for civil infrastructure. A set of wireless MICA2 mote accelerometers were deployed on a reinforced concrete bridge column to collect data during a shaking table earthquake simulation test. The data from the test were ingested into a data and metadata system for testing the usability of the metadata description for data mining and visualization. The metadata system used the NEESgrid metadata services interface for backend storage. The results of the data acquisition and ingestion were used to determine the feasibility of using a comprehensive sensor, data storage, and data retrieval system for health monitoring.