The response of buildings to tunnelling: A case study

A case study of the response of two buildings to the construction of a 12 m diameter tunnel excavated by conventional method, in Italy, is studied. The 12 m diameter tunnel was constructed carrying out reinforcement of the tunnel face and around the crown prior to excavation and installation of the temporary sprayed concrete lining and the permanent reinforced concrete lining. Reflective prisms, placed at first floor level around the perimeter of the building facades, allowed building settlements to be measured. Ground settlements between the two buildings were measured using BRE type settlement studs. Extensive protective measures were adopted to maintain stability of the tunnel excavation and to reduce ground movements. The number of horizontal jet grout columns installed into the tunnel face was reduced over the course of the project. Results from CPT tests indicate that the undrained shear strength at the tunnel axis is around 120 kPa. SPT and undrained unconsolidated (UU) triaxial tests indicate lower strengths of around 80 kPa, although this may be due to sample disturbance.

Solid-state memories based on ferroelectric tunnel junctions.

Ferroic-order parameters are useful as state variables in non-volatile information storage media because they show a hysteretic dependence on their electric or magnetic field. Coupling ferroics with quantum-mechanical tunnelling allows a simple and fast readout of the stored information through the influence of ferroic orders on the tunnel current. For example, data in magnetic random-access memories are stored in the relative alignment of two ferromagnetic electrodes separated by a non-magnetic tunnel barrier, and data readout is accomplished by a tunnel current measurement. However, such devices based on tunnel magnetoresistance typically exhibit OFF/ON ratios of less than 4, and require high powers for write operations (>1 × 10(6) A cm(-2)). Here, we report non-volatile memories with OFF/ON ratios as high as 100 and write powers as low as ∼1 × 10(4) A cm(-2) at room temperature by storing data in the electric polarization direction of a ferroelectric tunnel barrier. The junctions show large, stable, reproducible and reliable tunnel electroresistance, with resistance switching occurring at the coercive voltage of ferroelectric switching. These ferroelectric devices emerge as an alternative to other resistive memories, and have the advantage of not being based on voltage-induced migration of matter at the nanoscale, but on a purely electronic mechanism.

The effects of a second tunnel on the propagation of ground-borne vibration from an underground railway

Accurate predictions of ground-borne vibration levels in the vicinity of an underground railway are greatly sought in modern urban centers. Yet the complexity involved in simulating the underground environment means that it is necessary to make simplifying assumptions about this environment. One such commonly-made assumption is to model the railway as a single tunnel, despite many underground railway lines consisting of twin-bored tunnels. A unique model for two tunnels embedded in a homogeneous, elastic full space is developed. The vibration response of this two-tunnel system is calculated using the superposition of two displacement fields: one resulting from the forces acting on the invert of a single tunnel, and the other resulting from the interaction between the tunnels. By partitioning of the stresses into symmetric and anti-symmetric mode number components using Fourier decomposition, these two displacement fields can by calculated with minimal computational requirements. The significance of the interactions between twin-tunnels is quantified by calculating the insertion gains that result from the existence of a second tunnel. The insertion-gain results are shown to be localized and highly dependent on frequency, tunnel orientation and tunnel thickness. At some locations, the magnitude of these insertion gains is greater than 20dB. This demonstrates that a high degree of inaccuracy exists in any surface vibration-prediction model that includes only one of the two tunnels. © 2012 Springer.

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters. © 2011 Elsevier Ltd. All rights reserved.

Solid-state memories based on ferroelectric tunnel junctions

Ferroic-order parameters are useful as state variables in non-volatile information storage media because they show a hysteretic dependence on their electric or magnetic field. Coupling ferroics with quantum-mechanical tunnelling allows a simple and fast readout of the stored information through the influence of ferroic orders on the tunnel current. For example, data in magnetic random-access memories are stored in the relative alignment of two ferromagnetic electrodes separated by a non-magnetic tunnel barrier, and data readout is accomplished by a tunnel current measurement. However, such devices based on tunnel magnetoresistance typically exhibit OFF/ON ratios of less than 4, and require high powers for write operations (>1 × 10 6 A cm -2). Here, we report non-volatile memories with OFF/ON ratios as high as 100 and write powers as low as ∼1 × 10 4A cm -2 at room temperature by storing data in the electric polarization direction of a ferroelectric tunnel barrier. The junctions show large, stable, reproducible and reliable tunnel electroresistance, with resistance switching occurring at the coercive voltage of ferroelectric switching. These ferroelectric devices emerge as an alternative to other resistive memories, and have the advantage of not being based on voltage-induced migration of matter at the nanoscale, but on a purely electronic mechanism. © 2012 Macmillan Publishers Limited. All rights reserved.

Reinforcement learning with reference tracking control in continuous state spaces

The contribution described in this paper is an algorithm for learning nonlinear, reference tracking, control policies given no prior knowledge of the dynamical system and limited interaction with the system through the learning process. Concepts from the field of reinforcement learning, Bayesian statistics and classical control have been brought together in the formulation of this algorithm which can be viewed as a form of indirect self tuning regulator. On the task of reference tracking using a simulated inverted pendulum it was shown to yield generally improved performance on the best controller derived from the standard linear quadratic method using only 30 s of total interaction with the system. Finally, the algorithm was shown to work on the simulated double pendulum proving its ability to solve nontrivial control tasks. © 2011 IEEE.