无横撑下承式系杆拱侧倾稳定实用计算方法

以无横撑下承式系杆拱桥侧倾稳定问题为背景 ,用能量变分方法导出了其侧倾稳定的近似解析解 ,并通过具体算例 ,比较了由于吊杆的非保向力作用对其侧倾稳定性的影响 ,并将本文方法与工程设计中常用方法作了比较     

遗传算法在公路工程工期成本优化问题中的应用

利用遗传算法对公路工程工期成本优化问题进行探讨,给出利用遗传算法对整个问题的求解设计思路,在公路工程工期成本优化中利用遗传算法优化时染色体设计、优秀个体的充分利用以及遗传操作后不合理结果的处理提出解决方法,为今后遗传算法在公路工程进度优化中的应用提供有益的思路。最后设计求解流程图。     

遗传算法在区段站到发线的应用研究

为解决车站到发线的合理运用问题，采用遗传算建立了到发线运用的染色体结构和适应度函数，对这种复杂的非线性组合优化问题进行了仿真。     

城市公交线网优化的线性模型

为了对现有公交线网进行优化,有效利用现有交通资源,解决城市交通问题,运用系统科学的思想,通过对城市公交线网优化的主要内容、优化原则、优化目标以及约束条件的分析,兼顾考虑乘客出行时间、公交线网密度和公交企业的利益以及公交线网的布局对整个城市的交通系统的影响,提出了优化目标的函数表达式及相应约束条件的数学表达式,建立了公交线网优化的线性模型,并给出了运用逐步筛选法对所提出的数学模型进行求解的方法。     

聚丙烯纤维混凝土直接拉伸性能的试验研究

重点研究聚丙烯纤维增强混凝土在单轴直接拉伸荷载下的力学性能和纤维混凝土的单轴拉伸应力变形全曲线。提出单轴拉伸相对应力裂缝宽度曲线的理论方程式。由单轴拉伸全曲线得到了纤维混凝土的应力裂缝宽度曲线、断裂能及特征长度等。试验发现:当纤维体积掺量为0 14%时,纤维混凝土的轴心抗拉强度比基准混凝土提高20%,极限拉伸应变提高49%,断裂能提高68%,临界断裂时的最大裂缝宽度增加55%。聚丙烯纤维具有良好的阻裂性能,增强了硬化混凝土的能量吸收能力。     

电动汽车充电桩安全管理研究

随着国家对环境保护和能源节约的重视以及新能源政策的实施,电动汽车行业迎来了快速发展。充电基础设施的建设,是加快电动汽车推广的重要需要。电动汽车充电基础设施建设和运营业务发展的机会也极大,随之而来的安全管理工作要求也越来越高。本文依据充电桩建设和运营的特点,剖析其中面临的安全风险,并提出加强安全管理工作的措施,为充电桩企业安全管理运营提供参考和借鉴。     

Optimization of terminal airspace operation with environmental considerations

The rapid growth in air traffic has resulted in increased emission and noise levels in terminal areas, which brings negative environmental impact to surrounding areas. This study aims to optimize terminal area operations by taking into account environmental constraints pertaining to emission and noise. A multi-objective terminal area resource allocation problem is formulated by employing the arrival fix allocation (AFA) problem, while minimizing aircraft holding time, emission, and noise. The NSGA-II algorithm is employed to find the optimal assignment of terminal fixes with given demand input and environmental considerations, by incorporating the continuous descent approach (CDA). A case study of the Shanghai terminal area yields the following results: (1) Compared with existing arrival fix locations and the first-come-first-serve (FCFS) strategy, the AFA reduces emissions by 19.6%, and the areas impacted by noise by 16.4%. AFA and CDA combined reduce the emissions by 28% and noise by 38.1%; (2) Flight delays caused by the imbalance of demand and supply can be reduced by 72% (AFA) and 81% (AFA and CDA) respectively, compared with the FCFS strategy. The study demonstrates the feasibility of the proposed optimization framework to reduce the environmental impact in terminal areas while improving the operational efficiency, as well as its potential to underpin sustainable air traffic management.     

Methods to reduce dimensionality and identify candidate solutions in multi-objective signal timing problems

Adjusting traffic signal timings is a practical way for agencies to manage urban traffic without the need for significant infrastructure investments. Signal timings are generally selected to minimize the total control delay vehicles experience at an intersection, particularly when the intersection is isolated or undersaturated. However, in practice, there are many other potential objectives that might be considered in signal timing design, including: total passenger delay, pedestrian delays, delay inequity among competing movements, total number of stopping maneuvers, among others. These objectives do not tend to share the same relationships with signal timing plans and some of these objectives may be in direct conflict. The research proposes the use of a new multi-objective optimization (MOO) visualization technique—the mosaic plot—to easily quantify and identify significant tradeoffs between competing objectives using the set of Pareto optimal solutions that are normally provided by MOO algorithms. Using this tool, methods are also proposed to identify and remove potentially redundant or unnecessary objectives that do not have any significant tradeoffs with others in an effort to reduce problem dimensionality. Since MOO procedures will still be needed if more than one objective remains and MOO algorithms generally provide a set of candidate solutions instead of a single final solution, two methods are proposed to rank the set of Pareto optimal solutions based on how well they balance between the competing objectives to provide a final recommendation. These methods rely on converting the objectives to dimensionless values based on the optimal value for each specific objectives, which allows for direct comparison between and weighting of each. The proposed methods are demonstrated using a simple numerical example of an undersaturated intersection where all objectives can be analytically obtained. However, they can be readily applied to other signal timing problems where objectives can be obtained using simulation outputs to help identify the signal timing plan that provides the most reasonable tradeoff between competing objectives.