Seismic behaviour of rigid-faced reinforced soil retaining wall models:reinforcement effect

This paper presents the results of shaking table tests on models of rigid-faced reinforced soil retaining walls in which reinforcement materials of different tensile strength were used. The construction of the model retaining walls in a laminar box mounted on a shaking table, the instrumentation and the results from the shaking table tests are described in detail and the effects of the reinforcement parameters on the acceleration response at different elevations of the retaining wall, horizontal soil pressures and face deformations are presented. It was observed from these tests that the horizontal face displacement response of the rigid-faced retaining walls was significantly affected by the inclusion of reinforcement and even low-strength polymer reinforcement was found to be efficient in significantly reducing the deformation of the face. The acceleration amplifications were, however, observed to be less influenced by the reinforcement parameters. The results obtained from this study are helpful in understanding the relative performance of reinforced soil retaining walls under the different test conditions used in the experiments.

Effect of reinforcement form on the bearing capacity of square footings on sand

This paper presents the results of laboratory model loading tests and numerical studies carried out on square footings supported on geosynthetic reinforced sand beds. The relative performance of different forms of geosynthetic reinforcement (i.e. geocell, planar layers and randomly distributed mesh elements) in foundation beds is compared; using same quantity of reinforcement in each test. A biaxial geogrid and a geonet are used for reinforcing the sand beds. Geonet is used in two forms of reinforcement, viz. Planar layers and geocell, while the biaxial geogrid was used in three forms of reinforcement, viz. planar layers, geocell and randomly distributed mesh elements. Laboratory load tests on unreinforced and reinforced footings are simulated in a numerical model and the results are analyzed to understand the distribution of displacements and stresses below the footing better. Both the experimental and numerical studies demonstrated that the geocell is the most advantageous form of soil reinforcement technique of those investigated, provided there is no rupture of the material during loading. Geogrid used in the form of randomly distributed mesh elements is found to be inferior to the other two forms. Some significant observations on the difference in reinforcement mechanism for different forms of reinforcement are presented in this paper.

Optimal non-linear reinforcement schemes for stochastic automata

Two optimal non-linear reinforcement schemes—the Reward-Inaction and the Penalty-Inaction—for the two-state automaton functioning in a stationary random environment are considered. Very simple conditions of symmetry of the non-linear function figuring in the reinforcement scheme are shown to be necessary and sufficient for optimality. General expressions for the variance and rate of learning are derived. These schemes are compared with the already existing optimal linear schemes in the light of average variance and average rate of learning.

A reinforcement learning based algorithm for finite horizon Markov decision processes

We develop a simulation based algorithm for finite horizon Markov decision processes with finite state and finite action space. Illustrative numerical experiments with the proposed algorithm are shown for problems in flow control of communication networks and capacity switching in semiconductor fabrication.

Effect of densification and TiB2 reinforcement on the wear of molybdenum disilicide

Wear tests were done in a pin-on-disc machine by sliding MoSi2 pins against hard-steel discs in a normal load range of 5-140 N and a speed of 0.5 m/s under nominally dry conditions in the ambient. The specific wear rate of the pin undergoes two transitions: severe to mild at low load and mild to severe at high load. The mild-wear domain is distinguished by the formation of a protective mechanically mixed layer of steel and its oxides, transferred from the counterface in particulate form. Increasing the hardness by densification and TiB2 reinforcement lowers the specific wear rate and expands the mild-wear load domain. However, even when the volume wear rate is normalised with respect to the real contact area (load/hardness) the non-dimensional wear factor is still seen to decrease with densification and reinforcement. This indicates that fracture toughness may also play an important role in determining the wear-resistance of these materials. The surface coverage on the pin by the mechanically mixed layer increases with densification and reinforcement.

Processing and fiber content effects on the machinability of compression moulded random direction short GFRP composites

The random direction short Glass Fiber Reinforced Plastics (GFRP) have been prepared by two compression moulding processes, namely the Preform and Sheet Moulding Compound (SMC) processes. Cutting force analysis and surface characterization are conducted on the random direction short GFRPs with varying fiber contents (25 similar to 40%). Edge trimming experiments are preformed using carbide inserts with varing the depth of cut and cutting speed. Machining characteristics of the Preform and SMC processed random direction short GFRPs are evaluated in terms of cutting forces, surface quality, and tool wear. It is found that composite primary processing and fiber contents are major contributing factors influencing the cutting force magnitudes and surface textures. The SMC composites show better surface finish over the Preform composites due to less delamination and fiber pullouts. Moreover, matrix damage and fiber protrusions at the machined edge are reduced by increasing fiber content in the random direction short GFRP composites.

A tutorial survey of reinforcement learning

This paper gives a compact, self-contained tutorial survey of reinforcement learning, a tool that is increasingly finding application in the development of intelligent dynamic systems. Research on reinforcement learning during the past decade has led to the development of a variety of useful algorithms. This paper surveys the literature and presents the algorithms in a cohesive framework.

Reinforcement Learning With Function Approximation for Traffic Signal Control

We propose, for the first time, a reinforcement learning (RL) algorithm with function approximation for traffic signal control. Our algorithm incorporates state-action features and is easily implementable in high-dimensional settings. Prior work, e. g., the work of Abdulhai et al., on the application of RL to traffic signal control requires full-state representations and cannot be implemented, even in moderate-sized road networks, because the computational complexity exponentially grows in the numbers of lanes and junctions. We tackle this problem of the curse of dimensionality by effectively using feature-based state representations that use a broad characterization of the level of congestion as low, medium, or high. One advantage of our algorithm is that, unlike prior work based on RL, it does not require precise information on queue lengths and elapsed times at each lane but instead works with the aforementioned described features. The number of features that our algorithm requires is linear to the number of signaled lanes, thereby leading to several orders of magnitude reduction in the computational complexity. We perform implementations of our algorithm on various settings and show performance comparisons with other algorithms in the literature, including the works of Abdulhai et al. and Cools et al., as well as the fixed-timing and the longest queue algorithms. For comparison, we also develop an RL algorithm that uses full-state representation and incorporates prioritization of traffic, unlike the work of Abdulhai et al. We observe that our algorithm outperforms all the other algorithms on all the road network settings that we consider.

A finite element assessment of flexural strength of prestressed concrete beams with fiber reinforcement

This paper presents an assessment of the flexural behavior of 15 fully/partially prestressed high strength concrete beams containing steel fibers investigated using three-dimensional nonlinear finite elemental analysis. The experimental results consisted of eight fully and seven partially prestressed beams, which were designed to be flexure dominant in the absence of fibers. The main parameters varied in the tests were: the levels of prestressing force (i.e, in partially prestressed beams 50% of the prestress was reduced with the introduction of two high strength deformed bars instead), fiber volume fractions (0%, 0.5%, 1.0% and 1.5%), fiber location (full depth and partial depth over full length and half the depth over the shear span only). A three-dimensional nonlinear finite element analysis was conducted using ANSYS 5.5 [Theory Reference Manual. In: Kohnke P, editor. Elements Reference Manual. 8th ed. September 1998] general purpose finite element software to study the flexural behavior of both fully and partially prestressed fiber reinforced concrete beams. Influence of fibers on the concrete failure surface and stress-strain response of high strength concrete and the nonlinear stress-strain curves of prestressing wire and deformed bar were considered in the present analysis. In the finite element model. tension stiffening and bond slip between concrete and reinforcement (fibers., prestressing wire, and conventional reinforcing steel bar) have also been considered explicitly. The fraction of the entire volume of the fiber present along the longitudinal axis of the prestressed beams alone has been modeled explicitly as it is expected that these fibers would contribute to the mobilization of forces required to sustain the applied loads across the crack interfaces through their bridging action. A comparison of results from both tests and analysis on all 15 specimens confirm that, inclusion of fibers over a partial depth in the tensile side of the prestressed flexural structural members was economical and led to considerable cost saving without sacrificing on the desired performance. However. beams having fibers over half the depth in only the shear span, did not show any increase in the ultimate load or deformational characteristics when compared to plain concrete beams. (C) 2002 Published by Elsevier Science Ltd.

A reinforcement learning approach to automatic generation control

This paper formulates the automatic generation control (AGC) problem as a stochastic multistage decision problem. A strategy for solving this new AGC problem formulation is presented by using a reinforcement learning (RL) approach This method of obtaining an AGC controller does not depend on any knowledge of the system model and more importantly it admits considerable flexibility in defining the control objective. Two specific RL based AGC algorithms are presented. The first algorithm uses the traditional control objective of limiting area control error (ACE) excursions, where as, in the second algorithm, the controller can restore the load-generation balance by only monitoring deviation in tie line flows and system frequency and it does not need to know or estimate the composite ACE signal as is done by all current approaches. The effectiveness and versatility of the approaches has been demonstrated using a two area AGC model. (C) 2002 Elsevier Science B.V. All rights reserved.

Design of Geocell Reinforcement for Supporting Embankments on Soft Ground

The methods of design available for geocell-supported embankments are very few. Two of the earlier methods are considered in this paper and a third method is proposed and compared with them. The first method is the slip line method proposed by earlier researchers. The second method is based on slope stability analysis proposed by this author earlier and the new method proposed is based on the finite element analyses. In the first method, plastic bearing failure of the soil was assumed and the additional resistance due to geocell layer is calculated using a non-symmetric slip line field in the soft foundation soil. In the second method, generalpurpose slope stability program was used to design the geocell mattress of required strength for embankment using a composite model to represent the shear strength of geocell layer. In the third method proposed in this paper, geocell reinforcement is designed based on the plane strain finite element analysis of embankments. The geocell layer is modelled as an equivalent composite layer with modified strength and stiffness values. The strength and dimensions of geocell layer is estimated for the required bearing capacity or permissible deformations. These three design methods are compared through a design example.

Reinforcement learning with average cost for adaptive control of traffic lights at intersections

We propose for the first time two reinforcement learning algorithms with function approximation for average cost adaptive control of traffic lights. One of these algorithms is a version of Q-learning with function approximation while the other is a policy gradient actor-critic algorithm that incorporates multi-timescale stochastic approximation. We show performance comparisons on various network settings of these algorithms with a range of fixed timing algorithms, as well as a Q-learning algorithm with full state representation that we also implement. We observe that whereas (as expected) on a two-junction corridor, the full state representation algorithm shows the best results, this algorithm is not implementable on larger road networks. The algorithm PG-AC-TLC that we propose is seen to show the best overall performance.

Reinforcement learning applications in dynamic pricing of retail markets

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.