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.

Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective.

The tendency to make unhealthy choices is hypothesized to be related to an individual's temporal discount rate, the theoretical rate at which they devalue delayed rewards. Furthermore, a particular form of temporal discounting, hyperbolic discounting, has been proposed to explain why unhealthy behavior can occur despite healthy intentions. We examine these two hypotheses in turn. We first systematically review studies which investigate whether discount rates can predict unhealthy behavior. These studies reveal that high discount rates for money (and in some instances food or drug rewards) are associated with several unhealthy behaviors and markers of health status, establishing discounting as a promising predictive measure. We secondly examine whether intention-incongruent unhealthy actions are consistent with hyperbolic discounting. We conclude that intention-incongruent actions are often triggered by environmental cues or changes in motivational state, whose effects are not parameterized by hyperbolic discounting. We propose a framework for understanding these state-based effects in terms of the interplay of two distinct reinforcement learning mechanisms: a "model-based" (or goal-directed) system and a "model-free" (or habitual) system. Under this framework, while discounting of delayed health may contribute to the initiation of unhealthy behavior, with repetition, many unhealthy behaviors become habitual; if health goals then change, habitual behavior can still arise in response to environmental cues. We propose that the burgeoning development of computational models of these processes will permit further identification of health decision-making phenotypes.