Uplift resistance of buried pipelines at low cover-diameter ratios

Reliable estimates for the maximum available uplift resistance from the backfill soil are essential to prevent upheaval buckling of buried pipelines. The current design code DNV RP F110 does not offer guidance on how to predict the uplift resistance when the cover:pipe diameter (H/D) ratio is less than 2. Hence the current industry practice is to discount the shear contribution from uplift resitance for design scenarios with H/D ratios less than 1. The necessity of this extra conservatism is assessed through a series of full-scale and centrifuge tests, 21 in total, at the Schofield Centre, University of Cambridge. Backfill types include saturated loose sand, saturated dense sand and dry gravel. Data revealed that the Vertical Slip Surface Model remains applicable for design scenarios in loose sand, dense sand and gravel with H/D ratios less than 1, and that there is no evidence that the contribution from shear should be ignored at these low H/D ratios. For uplift events in gravel, the shear component seems reliable if the cover is more than 1-2 times the average particle size (D50), and more research effort is currenty being carried out to verify this conclusion. Strain analysis from the Particle Image Velocimetry (PIV) technique proves that the Vertical Slip Surface Model is a good representation of the true uplift deformation mechanism in loose sand at H/D ratios between 0.5 and 3.5. At very low H/D ratios (H/D < 0.5), the deformation mechanism is more wedge-like, but the increased contribution from soil weight is likely to be compensated by the reduced shear contributions. Hence the design equation based on the Vertical Slip Surface Model still produces good estimates for the maximum available uplift resistance. The evolution of shear strain field from PIV analysis provides useful insight into how uplift resistance is mobilized as the uplift event progresses. Copyright 2010, Offshore Technology Conference.

Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.