Joint leakage is one of the major problems for shield tunnels. This paper proposes a novel approach to assessing the sealant performance of gasketed joints based on the A-star search algorithm. By treating the contact...
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Joint leakage is one of the major problems for shield tunnels. This paper proposes a novel approach to assessing the sealant performance of gasketed joints based on the A-star search algorithm. By treating the contact surface of gaskets as the search space of the optimal leakage path, the water leakage process is modeled as a path optimization task informed by the nonuniform distribution of contact stresses. This new approach determines the optimal leakage path together with the associated leakage threshold. The validity of the proposed algorithm is demonstrated through a comparison with experimental test results, followed by a comprehensive parametric study of joint opening, offset, and self-sealing effect. The results elucidate the dominant role of localized contact in sealant performance, which is notably affected by joint deformation. Scenarios of large offsets and small openings exhibit abnormal increases in leakage thresholds and elongated leakage paths due to additional gasketto-groove contacts and increased heterogeneity of contact stress distribution. The self-sealing process exerts a pronounced nonlinear impact on the sealant performance of gasketed joints, leading to a rapid escalation of the leakage threshold when the lateral water pressure surpasses 0.5 MPa. Additionally, lateral pressurization induces nonuniform distribution of non-conductive regions, resulting in convoluted optimal leakage paths.
This study presents a novel modification of the A* path planning algorithm that integrates ergonomic and radiological constraints to enhance worker safety in nuclear decommissioning environments. The algorithm optimiz...
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This study presents a novel modification of the A* path planning algorithm that integrates ergonomic and radiological constraints to enhance worker safety in nuclear decommissioning environments. The algorithm optimizes paths by incorporating ergonomic scores derived from biomechanical data alongside radiation exposure estimates from simulations. To assess the impact of load carriage, worker performance is analyzed under normal and weighted walking conditions, with a focus on joint biomechanics, including moment, power, angle, and velocity. Results indicate that while cumulative radiation exposure remains relatively stable, weighted walking significantly increases biomechanical strain, elevating the risk of musculoskeletal disorders (MSDs). The proposed dual-cost optimization approach provides a comprehensive framework for balancing radiation exposure and ergonomic stress, offering practical improvements for worker safety and efficiency in hazardous environments.
Frost is one of the severe weather events causing economic losses in agriculture. Traditionally, heating can be applied in an orchard during a frost event either using fixed heaters or mobile heaters to travel through...
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Frost is one of the severe weather events causing economic losses in agriculture. Traditionally, heating can be applied in an orchard during a frost event either using fixed heaters or mobile heaters to travel through. However, both traditional heating strategies have limited heating capacity. In this study, a precision heating strategy was proposed to prioritise applying heat to orchard canopies with high heating demands utilising multiple heaters. The precision heating experiment in a simulated orchard environment with dynamic multiple goal points was implemented by an improved A-star path-planning algorithm, in which a path cost estimation method based on linear programming was presented to find the optimal paths for multiple heaters and a conflictbased search (CBS) method was used to generate collision-free paths. In addition, an estimation method for the heater number based on the precision heating strategy was proposed and the difference of the heater number between precision heating and traditional heating was compared. The simulated results show that the improved A-staralgorithm had higher search efficiency, resulting in 36.8% less total path cost and 98.7% less computational time than the control group. The comparison between precision heating and traditional heating indicates that the number of heaters used in the precision heating strategy decreased by 96.8% and 85.9% compared to the traditional fixed heating strategy and mobile heating strategy, respectively. Overall, this study provided a concept of high heat-demand priority precision heating and proved its superiority for frost protection.
This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm...
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This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly -exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two -level map' approach, where a lower -resolution grid map is derived from a high -resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target -biased strategy and dual -direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.
Wireless Sensor Networks (WSNs) emerged as major data gathering paradigm due to its wide variety of applications. Achieving energy efficient reliable data delivery is an important concern in WSNs. In a net-work, packe...
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Wireless Sensor Networks (WSNs) emerged as major data gathering paradigm due to its wide variety of applications. Achieving energy efficient reliable data delivery is an important concern in WSNs. In a net-work, packet loss reduces the reliable data transmission rate. Retransmissions are required to increase the reliable data transmission rate. However, number of retransmissions also increase the network energy consumptions and packet delivery delay. Packet loss rate can be reduced by selecting a best suc-cessor node in a routing path. In this work, a Learning Automata based Routing mechanism is proposed for wireless sensor networks to achieve energy efficiency and reliable data delivery. In this paper, learn-ing automata has been adopted to calculate next node's selection probability in a routing path using node's score, quality of link and previous selection probability. Further, an energy efficient reliable rout-ing mechanism is proposed using combination of learning automata and A-star search algorithm. The proposed mechanism determines energy efficient and reliable routing paths in WSNs by favoring highest residual energy, good quality of link , more free buffer space and less distance. Finally, simulation results indicate that the proposed algorithm reduces the energy consumptions, data delivery delay, data trans-missions and increases the network lifetime.(c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
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