Tunnel engineering – influence of the type and the quantity of measurements in the back analysis of geomechanical parameters

The monitoring data collected during tunnel excavation can be used in inverse analysis procedures in order to identify more realistic geomechanical parameters that can increase the knowledge about the interested formations. These more realistic parameters can be used in real time to adapt the project to the real structure in situ behaviour. However, monitoring plans are normally designed for safety assessment and not especially for the purpose of inverse analysis. In fact, there is a lack of knowledge about what types and quantity of measurements are needed to succeed in identifying the parameters of interest. Also, the optimisation algorithm chosen for the identification procedure may be important for this matter. In this work, this problem is addressed using a theoretical case with which a thorough parametric study was carried out using two optimisation algorithms based on different calculation paradigms, namely a conventional gradient-based algorithm and an evolution strategy algorithm. Calculations were carried for different sets of parameters to identify several combinations of types and amount of monitoring data. The results clearly show the high importance of the available monitoring data and the chosen algorithm for the success rate of the inverse analysis process.

Study of natural ventilation for a modular façade system in wind tunnel tests

Wind tunnel tests are a reliable tool to determine the effect of natural ventilation on buildings. This paper presents results of wind tunnel tests conducted to evaluate the influence of ventilation modules positioning on a façade system. Modules positioning was modified, resulting in different façade configurations. The tests were carried out with the use of a model, varying the position of the ventilation modules in the façade configuration. The cases tested were six ventilation modules positioned below the window-sill (ventilated window-sill), and three ventilation modules positioned above and below the façade. The façade system proposed was movable and interchangeable so that the same basic model could be used to test the possibilities for ventilation. Wind speed measurements were taken inside and outside the model for the different façades configurations to evaluate the best performance in relation to natural ventilation. Singleâ sided and Cross ventilation were considered for wind speed measurements. Results show the use of six ventilation modules positioned below the window-sill, forming "a ventilated window-sill" is the best solution in terms of natural ventilation.

Natural fibers for soil reinforcement

The reinforcement of soil is defined as an effective and reliable technique to improve strength and stability. For this propose, the use of natural fibers has been commonly. Over the past years, a series of studies have been performed in order to investigate the influence of randomly oriented fibers, especially for compressible clayey soils. However, less attention has been given to the reinforcing of sandy materials, as well as the use of oriented fibers meshes in order to improve mechanical behaviour. The main aim of this study is to identify the influence that different percentages of fibers, as well as the use of meshes of oriented fibers, has on soil mechanical behaviour. For this purpose, unconfined compression tests with local strain measurements were performed on a silty sand reinforced with Sisal fibers and a comparative study between randomly oriented and 0° and 90° fibers is presented.

On the role of temporal context in human reinforcement learning

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2011

Solid-solid recuperation to improve the energy efficiency of tunnel kilns

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2011

Mathematical model to analyze the heat transfer in tunnel kilns for burning of ceramics

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2013

Testing the Tunnel Effect: Comparison, Age and Happiness in UK and German Panels

In contrast to previous results combining all ages we find positive effects of comparison income on happiness for the under 45s, and negative effects for those over 45. In the BHPS these coefficients are several times the magnitude of own income effects. In GSOEP they cancel to give no effect of effect of comparison income on life satisfaction in the whole sample, when controlling for fixed effects, and time-in-panel, and with flexible, age-group dummies. The residual age-happiness relationship is hump-shaped in all three countries. Results are consistent with a simple life cycle model of relative income under uncertainty.

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

Management of corn stalk waste as reinforcement for polypropylene injection moulded composites

The main objective of this study was the management of corn stalk waste as reinforcement for polypropylene (PP) injection moulded composites as an alternative to wood flour and fibers. In the first step, corn stalk waste was subjected to various treatments, and four different corn stalk derivatives (flour and fibers) able to be used as reinforcement of composite materials were prepared and characterized. These derivatives are corn stalk flour, thermo-mechanical, semi-chemical, and chemical fibers. They were characterized in terms of their yield, lignin content, Kappa number, fiber length/diameter ratio, fines, coarseness, viscosity, and the length at the break of a standard sheet of paper. Results showed that the corn stalk derivatives have different physico-chemical properties. In the second step, the prepared flour and fibers were explored as a reinforcing element for PP composites. Coupled and non-coupled PP composites were prepared and tested for tensile properties. For overall trend, with the addition of a coupling agent, tensile properties of composites significantly improved, as compared with non-coupled samples. In addition, a morphological study revealed the positive effect of the coupling agent on the interfacial bonding. The composites prepared with semichemical fiber gave better results in comparison with the rest of the corn stalk derivatives due to its chemical characteristics

CB1 cannabinoid receptor modulates MDMA acute responses and reinforcement

Background: 3, 4-methylenedioxymethamphetamine (MDMA) is a popular recreational drug widely abused by young people. The endocannabinoid system is involved in the addictive processes induced by different drugs of abuse. However, the role of this system in the pharmacological effects of MDMA has not been yet clarified.Methods: Locomotion, body temperature and anxiogenic-like responses were evaluated after acute MDMA administration in CB1 knockout mice. Additionally, MDMA rewarding properties were investigated in the place conditioning and the intravenous self-administration paradigms. Extracellular levels of DA in the nucleus accumbens were also analyzed after a single administration of MDMA by in vivo microdialysis. Results: Acute MDMA administration increased locomotor activity, body temperature and anxiogenic-like responses in wild type mice, but these responses were lower or abolished in knockout animals. MDMA produced similar conditioned place preference and increased dopamine extracellular levels in the nucleus accumbens in both genotypes. Nevertheless, CB1 knockout mice failed to self-administer MDMA at any of the doses used. Conclusions: These results indicate that CB1 cannabinoid receptors play an important role in the acute prototypical effects of MDMA, and are essential in the acquisition of an operant behavior to self-administer this drug.