The nonlinear stress-strain relationship of a thin plate or stiffened panel under in-plane load is represented by a load-shortening curve. The curves are used to evaluate the buckling and ultimate collapse behaviour of these structural elements, and furthermore forming the input data to analytical progressive collapse methods for large scale box girder structures such as ships. This paper develops a novel analytical method that predicts the load-shortening curve of plates and stiffened panels under cyclic in-plane load. This provides the framework to account for load reversals in an enhanced cyclic progressive collapse method. A parametric study using nonlinear finite element analysis is completed to investigate the characteristic behaviour of simply supported plates under cyclic compression and tension. The investigation covers a range of aspect ratios and slenderness ratios typical for ship-type structures. Single-cycle and ten-cycle loading protocols are applied, which demonstrate progressive reduction in strength and stiffness together with a response convergence after several cycles. An analytical method to predict multi-cycle load-shortening behaviour is then derived using a response and updating rule based on the observed characteristics from the parametric study. A validation of the analytical method is performed on a range of unstiffened plates and stiffened panels under various cyclic loading protocols. A good comparison with the results of finite element analysis is obtained, which confirms the validity of the proposed analytical method.

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Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context of GreenGPS; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in [1], however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5% over the fastest, 11.2% over the shortest, and 8.4% over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.

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