Run-out of landslides in brittle soils

One of the factors causing the acceleration of landslides is the loss of strength of the soil involved in the potential unstable mechanism. The travelled distance and the landslide velocity, a key factor in risk analysis, will be determined by the loss of resistant forces. Brittle behaviour, commonly associated with cemented soils, overconsolidated plastic clay formations and sensitive clays, lead to the progressive failure phenomenon explained by the reduction of the strength with increasing strain. In the present study, this phenomenon has been analysed in the case of a saturated slope which becomes unstable by increasing the boundary pore water pressure. A Mohr-Coulomb model with strain softening behaviour induced by increasing deviatoric plastic strain is used. The paper focusses not only on the stability of the slope but also on the post failure behaviour (run-out and sliding velocity). A coupled hydro-mechanical formulation of the material point method has been used to simulate the whole instability process. The influence of the brittleness of the material on the triggering of instability and run-out is evaluated by means of a parametric study varying peak and residual strength. The onset of the failure and the failure geometry are controlled by both peak and residual values. Good correlations between run-outs and brittleness are found. The decay of the strength determines the acceleration of the landslides and the travelled distance.Peer ReviewedPostprint (author's final draft

Natural Variation and Neuromechanical Systems

Natural variation plays an important but subtle and often ignored role in neuromechanical systems. This is especially important when designing for living or hybrid systems \ud which involve a biological or self-assembling component. Accounting for natural variation can be accomplished by taking a population phenomics approach to modeling and analyzing such systems. I will advocate the position that noise in neuromechanical systems is partially represented by natural variation inherent in user physiology. Furthermore, this noise can be augmentative in systems that couple physiological systems with technology. There are several tools and approaches that can be borrowed from computational biology to characterize the populations of users as they interact with the technology. In addition to transplanted approaches, the potential of natural variation can be understood as having a range of effects on both the individual's physiology and function of the living/hybrid system over time. Finally, accounting for natural variation can be put to good use in human-machine system design, as three prescriptions for exploiting variation in design are proposed

Characterizing Natural Fractures and Their Interactions with Hydraulically Induced Fractures

Natural fractures are preexisting micro-cracks and fissures that can have a critical impact on hydraulic fracture treatments in shales. Most shale formations contain natural fractures, but the characteristics of these natural fractures can vary significantly. For example, the natural fractures in the Barnett Shale are mostly narrow, long, and sealed with calcite cement. The natural fractures in the Wolfcamp Shale are much more heterogeneous as a whole, but tend to be clustered in similar groupings based on the lithology of certain areas of the formation. The creation and development of natural fractures prior to any hydraulic fracturing treatments is primarily a function of mineralogy, total organic carbon, and in-situ stresses. During hydraulic fracturing treatments, certain characteristics, such as the relative angle between the natural and hydraulic fractures, the length of the natural fractures, the differential stress of the formation rock, and certain completion design variables, will determine how the natural and induced fractures interact and create a fracture network. The creation of a natural fracture network can have a positive effect on the ultimate hydrocarbon recovery in some cases. Natural fractures provide accumulation space and travel pathways for hydrocarbons, which is critical in low porosity and low permeability shales. However, natural fractures can result in higher rates of fluid leakoff, which will result in less efficient hydraulic fracture treatments overall. Also, natural fractures can provide an undesirable connection to water accumulations, which can negatively impact the economics of a well because of the disposal costs associated with water production. This thesis seeks to characterize natural fractures and also to describe the author's work on a hydraulic fracture simulation software that takes the impact of natural fractures into account.Petroleum and Geosystems Engineerin

Using Synthetic Data to Train Neural Networks is Model-Based Reasoning

We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a neural network using synthetic data can be viewed as learning a proposal distribution generator for approximate inference in the synthetic-data generative model. We demonstrate this connection in a recognition task where we develop a novel Captcha-breaking architecture and train it using synthetic data, demonstrating both state-of-the-art performance and a way of computing task-specific posterior uncertainty. Using a neural network trained this way, we also demonstrate successful breaking of real-world Captchas currently used by Facebook and Wikipedia. Reasoning from these empirical results and drawing connections with Bayesian modeling, we discuss the robustness of synthetic data results and suggest important considerations for ensuring good neural network generalization when training with synthetic data.Comment: 8 pages, 4 figure

Logic, self-awareness and self-improvement: The metacognitive loop and the problem of brittleness

This essay describes a general approach to building perturbation-tolerant autonomous systems, based on the conviction that artificial agents should be able notice when something is amiss, assess the anomaly, and guide a solution into place. We call this basic strategy of self-guided learning the metacognitive loop; it involves the system monitoring, reasoning about, and, when necessary, altering its own decision-making components. In this essay, we (a) argue that equipping agents with a metacognitive loop can help to overcome the brittleness problem, (b) detail the metacognitive loop and its relation to our ongoing work on time-sensitive commonsense reasoning, (c) describe specific, implemented systems whose perturbation tolerance was improved by adding a metacognitive loop, and (d) outline both short-term and long-term research agendas

Intelligence, Control and the Artificial Mind

Artificial intelligence and cognitive science must look at the world of industrial-process control to find the technological reifications of the concept of mind

Crackling noise in three-point bending of heterogeneous materials

We study the crackling noise emerging during single crack propagation in a specimen under three-point bending conditions. Computer simulations are carried out in the framework of a discrete element model where the specimen is discretized in terms of convex polygons and cohesive elements are represented by beams. Computer simulations revealed that fracture proceeds in bursts whose size and waiting time distributions have a power law functional form with an exponential cutoff. Controlling the degree of brittleness of the sample by the amount of disorder, we obtain a scaling form for the characteristic quantities of crackling noise of quasi-brittle materials. Analyzing the spatial structure of damage we show that ahead of the crack tip a process zone is formed as a random sequence of broken and intact mesoscopic elements. We characterize the statistics of the shrinking and expanding steps of the process zone and determine the damage profile in the vicinity of the crack tip.Comment: 11 pages, 15 figure