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New Solutions to Optimum Vertical Curve Problem

机译:最佳垂直曲线问题的新解决方案

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Estimating curve parameters using observed data is of fundamental importance to surveyors. In 1999 Easa converted the L_1 estimation of parabolic curve parameters into a linear programming (LP) problem, which enabled a rapid solution by LINDO. However, this approach requires LP formulation and associated software that may not be easily inaccessible to all surveyors, while its efficiency declines when handling the nonlinear global problem with unknown start and end points (x_1 and x_2) of the curve. Easa sought the global solution by repeating the LP procedure over various combinations of x_1 and x_2 manually, using a step size of 5 m for each variable. With this approach, one faces a trade-off between labor and accuracy. As an alternative, a convenient spreadsheet method suitable for L_1, L_2, or "mini-max" optimization is developed here that reduces the labor and formulation involved and requires no LP language. The new approach is further automated by Visual Basic for Applications programming to solve the global problem, allowing a fine step size of 0.1 m to be used. The vast data set generated leads to an accurate 3D picture that reveals the true behavior of the global solution, which cannot be captured when a step size of 5 m is used.
机译:使用观测数据估算曲线参数对测量师至关重要。在1999年,Easa将抛物线曲线参数的L_1估计转换为线性规划(LP)问题,从而使LINDO能够快速解决问题。但是,这种方法要求LP公式和相关软件可能并非所有测量师都难以接近,而当处理未知曲线起点和终点(x_1和x_2)的非线性全局问题时,其效率会下降。 Easa通过手动对x_1和x_2的各种组合重复LP程序(每个变量的步长为5 m)来寻求全局解决方案。通过这种方法,人们需要在人工和准确性之间进行权衡。作为替代方案,此处开发了一种适用于L_1,L_2或“最小-最大”优化的便捷电子表格方法,该方法可减少所需的人工和配方,并且不需要LP语言。 Visual Basic for Applications编程进一步自动化了该新方法,以解决全局问题,从而允许使用0.1 m的精细步长。生成的庞大数据集可生成准确的3D图片,该图片揭示了全局解决方案的真实行为,当使用5 m的步长时无法捕获。

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