13 resultados para Road model

em QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast


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The performance of an air-cycle refrigeration unit for road transport, which had been previously reported, was analysed in detail and compared with the original design model and an equivalent Thermo King SL200 vapour-cycle refrigeration unit. Poor heat exchanger performance was found to be the major contributor to low coefficient of performance values. Using state-of-the-art, but achievable performance levels for turbomachinery and heat exchangers, the performance of an optimised air-cycle refrigeration unit for the same application was predicted. The power requirement of the optimised air-cycle unit was 7% greater than the equivalent vapour-cycle unit at full-load operation. However, at part-load operation the air-cycle unit was estimated to absorb 35% less power than the vapour-cycle unit. The analysis demonstrated that the air-cycle system could potentially match the overall fuel consumption of the vapour-cycle transport refrigeration unit, while delivering the benefit of a completely refrigerant free system.

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This article traces the strategic re-orientation of the Spanish Greens (Los Verdes) from a position of hostility toward the Socialists (PSOE) to one of collaboration that allowed them to gain parliamentary representation in the 2004 national and European elections. Drawing upon insights from the party politics literature, it schematizes a model and then proceeds to use it to provide a diachronic account of factionalist conflict within the ranks of the Greens and their close competitors, the structure of political opportunities, exogenous factors and their interrelationship, up to the point of the electoral agreement between the two parties. In concludes by highlighting the role played by the Madrid bombings in bringing the PSOE back into power and offers some projections about the future institutional access of the Greens.

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In this paper, we introduce a macroscopic model for road traffic accidents along highway sections. We discuss the motivation and the derivation of such a model, and we present its mathematical properties. The results are presented by means of examples where a section of a crowded one-way highway contains in the middle a cluster of drivers whose dynamics are prone to road traffic accidents. We discuss the coupling conditions and present some existence results of weak solutions to the associated Riemann Problems. Furthermore, we illustrate some features of the proposed model through some numerical simulations. © The authors 2012.

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This paper reports laboratory experiments designed to study the impact of public information about past departure rates on congestion levels and travel costs. Our design is based on a discrete version of Arnott et al.'s (1990) bottleneck model. In all treatments, congestion occurs and the observed travel costs are quite similar to the predicted ones. Subjects' capacity to coordinate is not affected by the availability of public information on past departure rates, by the number of drivers or by the relative cost of delay. This seemingly absence of treatment effects is confirmed by our finding that a parameter-free reinforcement learning model best characterises individual behaviour.

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In the interaction between vehicles, pavements and bridges, it is essential to aim towards a reduction of vehicle axle forces to promote longer pavement life spans and to prevent bridges loads becoming too high. Moreover, as the road surface roughness affects the vehicle dynamic forces, an efficient monitoring of pavement condition is also necessary to achieve this aim. This paper uses a novel algorithm to identify the dynamic interaction forces and pavement roughness from vehicle accelerations in both theoretical simulations and a laboratory experiment; moving force identification theory is applied to a vehicle model for this purpose. Theoretical simulations are employed to evaluate the ability of the algorithm to predict forces over a range of bridge spans and to evaluate the influence of road roughness level on the accuracy of the results. Finally, in addressing the challenge for the real-world problem, the effects of vehicle configuration and speed on the predicted road roughness are also investigated in a laboratory experiment.

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Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

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A periodic monitoring of the pavement condition facilitates a cost-effective distribution of the resources available for maintenance of the road infrastructure network. The task can be accurately carried out using profilometers, but such an approach is generally expensive. This paper presents a method to collect information on the road profile via accelerometers mounted in a fleet of non-specialist vehicles, such as police cars, that are in use for other purposes. It proposes an optimisation algorithm, based on Cross Entropy theory, to predict road irregularities. The Cross Entropy algorithm estimates the height of the road irregularities from vehicle accelerations at each point in time. To test the algorithm, the crossing of a half-car roll model is simulated over a range of road profiles to obtain accelerations of the vehicle sprung and unsprung masses. Then, the simulated vehicle accelerations are used as input in an iterative procedure that searches for the best solution to the inverse problem of finding road irregularities. In each iteration, a sample of road profiles is generated and an objective function defined as the sum of squares of differences between the ‘measured’ and predicted accelerations is minimized until convergence is reached. The reconstructed profile is classified according to ISO and IRI recommendations and compared to its original class. Results demonstrate that the approach is feasible and that a good estimate of the short-wavelength features of the road profile can be detected, despite the variability between the vehicles used to collect the data.

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The axle forces applied by a vehicle through its wheels are a critical part of the interaction between vehicles, pavements and bridges. Therefore, the minimisation of these forces is important in order to promote long pavement life spans and ensure that bridge loads are small. Moreover, as the road surface roughness affects the vehicle dynamic forces, the monitoring of pavements for highways and bridges is an important task. This paper presents a novel algorithm to identify these dynamic interaction forces which involves direct instrumentation of a vehicle with accelerometers. The ability of this approach to predict the pavement roughness is also presented. Moving force identification theory is applied to a vehicle model in theoretical simulations in order to obtain the interaction forces and pavement roughness from the measured accelerations. The method is tested for a range of bridge spans in simulations and the influence of road roughness level on the accuracy of the results is investigated. Finally, the challenge for the real-world problem is addressed in a laboratory experiment.

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This paper describes the hydrogeological processes which caused unexpected instability and quick conditions during the excavation of a 25m deep cutting through a drumlin in County Down, Northern Ireland. A conceptual hydrogeological model of the cutting, based on pore pressures monitored during and after the excavation demonstrates how quick conditions at the toe of the cutting caused liquefaction of the till. Stability of the cutting was re-established by draining the highly permeable, weathered Greywacke which underlies the drumlin, through the use of a deep toe drain. In spite of this drainage, the cutting was only marginally stable due to the presence of a low permeability zone in the till above the bedrock which limits the reduction of elevated pore pressures within the upper to mid-depths of the drumlin. The factor of safety has been further improved by the addition of vertical relief drains at the crest and berm of the cutting to relieve the pore-pressures within the upper till by intercepting the weathered bedrock. The paper also highlights the importance of carrying out an adequate site investigation compliant with Eurocode 7 and additional monitoring in excavations in stiff, low permeability till.