跨河大跨度预应力混凝土桥的施工

该文详细介绍了大芦线航道整治一期工程18标桥梁施工过程和施工技术。叙述了河道回填、基础施工、临时锁定柱、立柱、大跨度预应力混凝土连续梁及引桥段简支梁的施工工艺。其施工工艺对类似工程的施工有借鉴意义。     

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引松供水工程4标段隧洞衬砌形式分为钢筋混凝土衬砌及压力钢管衬砌。钢管衬砌施工段距隧洞出口长度约为5.55 km，钢管衬砌直径为5.61 m，与隧洞内径最小间距仅为30 cm。为解决在狭小空间内大直径钢管运距长、定位精度低、支撑不牢固和拱顶填充不密实等技术难题，通过分析、总结影响压力钢管衬砌快速施工的因素及解决措施，对比研究多种施工方案，结合工程实践，提出并研制液压轨行式钢管运输台车，同时采用自密实混凝土及米字型支撑加固等一系列措施确保压力钢管满足快速衬砌施工的条件。经工程验证，该措施能够有效解决上述难题，不仅提高了施工效率，而且减小了主线工期压力，为本工程的顺利通水奠定了坚实的基础。     

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清江水布垭导流洞洞身穿越软硬相间的近乎水平层面的栖霞组岩层 ,地质构造复杂 ,洞身开挖跨度大。施工单位依据科学探索 ,安全、优质、快速地完成导流洞的开挖。详细介绍具体的施工过程。     

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Vehicle identification sensors location problem for large networks

Finding the optimal location for sensors is a key problem in flow estimation. There are several location models that have been developed recently for vehicle identification (ID) sensors. However, these location models cannot be applied to large networks because there are many constraints and integer variables. Based on a property of the location problem for vehicle ID sensors, given the initial vehicle ID sensors that are pre-installed and fixed on the network, this article presents a solution that greatly reduces the size of this location problem. An applied example demonstrates that when 8% of the arcs from a real network that are randomly selected have a vehicle ID sensor, the reductions are as large as 97% for the number of remaining constraints in the location model and 84% for the adjusted diameter of the feasible region of target flow. Using these two indices as target functions, two greedy algorithms are presented for solving the vehicle ID sensor location problem. These two algorithms were applied to an example in Mashhad city with 2,526 arcs, 7,157 origin-destination pairs and 121,627 paths. Using these algorithms, installing vehicle ID sensors on 8% of the network arcs results in satisfaction of 99.82% of the constraints in the location model and 97.6% reduction in the adjusted maximum possible error index. This means that deploying a low number of vehicle ID sensors on a real large network, with these greedy algorithms, yields a high level of observability.